thesis topics on natural language processing

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• Innovative 12+ Natural Language Processing Thesis Topics

Generally, natural language processing is the sub-branch of Artificial Intelligence (AI). Natural language processing is otherwise known as NLP. It is compatible in dealing with multi-linguistic aspects and they convert the text into binary formats in which computers can understand it.  Primarily, the device understands the texts and then translates according to the questions asked. These processes are getting done with the help of several techniques. As this article is concentrated on delivering the natural language processing thesis topics , we are going to reveal each and every aspect that is needed for an effective NLP thesis .

NLP has a wide range of areas to explore in which enormous researches will be conducted. As the matter of fact, they analyses emotions, processes images, summarize texts, answer the questions & translates automatically, and so on.

Thesis writing is one of the important steps in researches. As they can deliver the exact perceptions of the researcher to the opponents hence it is advisable to frame the proper one. Let us begin this article with an overview of the NLP system . Are you ready to sail with us? Come on, guys!!!

“This is the article which is framed to the NLP enthusiasts in order to offer the natural language processing thesis topics”

What is Actually an NLP?

• NLP is the process of retrieving the meaning of the given sentence
• For this they use techniques & algorithms in order to extract the features
• They are also involved with the following,
• Audio capturing
• Text processing
• Conversion of audio into text
• Human-computer interaction

This is a crisp overview of the NLP system. NLP is one of the major technologies that are being used in the day to day life. Without these technologies, we could not even imagine a single scenario . In fact, they minimized the time of human beings by means of spelling checks, grammatical formations and most importantly they are highly capable of handling audio data . In this regard, let us have an idea of how does the NLP works in general. Shall we get into that section? Come let’s move on to that!!!

How does NLP Works?

• Unstructured Data Inputs
• Lingual Knowledge
• Domain Knowledge
• Domain Model
• Corpora Model Training
• Tools & Methods

The above listed are necessary when input is given to the model. The NLP model is in need of the above-itemized aspects to process the unstructured data in order to offer the structured data by means of parsing, stemming and lemmatization, and so on. In fact, NLP is subject to the classifications by their eminent features such as generation & understanding.  Yes my dear students we are going to cover the next sections with the NLP classifications.  

Classifications of NLP

• Natural Language-based Generation
• Natural Language-based Understanding

The above listed are the 2 major classifications of NLP technology . In these classifications let us have further brief explanations of the natural language-based understanding for your better understanding.

• Biometric Domains
• Spam Detection
• Opinion/Data Mining
• Entity Linking
• Named Entity Recognition
• Relationship Extraction

This is how the natural language-based understanding is sub-classified according to its functions. In recent days, NLP is getting boom in which various r esearches and projects are getting investigated and implemented successfully by our technical team. Generally, NLP processes are getting performed in a structural manner. That means they are overlays in several steps in crafting natural language processing thesis topics . Yes dears, we are going to envelop the next section with the steps that are concreted with the natural language processing.

NLP Natural Language Processing Steps

• Segmentation of Sentences
• Tokenization of Words
• PoS Tagging
• Parsing of Syntactic Contexts
• Removing of Stop Words
• Lemmatization & Stemming
• Classification of Texts
• Emotion/Sentiment Analysis

Here POS stands for the Parts of Speech . These are some of the steps involved in natural language processing. NLP performs according to the inputs given. Here you might need examples in these areas. For your better understanding, we are going to illustrate to you about the same with clear bulletin points. Come let us try to understand them.

• Let we take inputs as text & speech
• Text inputs are analyzed by “word tokenization”
• Speech inputs are analyzed by “phonetics”

In addition to that, they both are further processed in the same manner as they are,

• Morphological Analysis
• Syntactic Analysis
• Semantic Understanding
• Speech Processing

The above listed are the steps involved in NLP tasks in general . Word tokenization is one of the major which points out the vocabulary words presented in the word groups . Though, NLP processes are subject to numerous challenges. Our technical team is pointed out to you the challenges involved in the current days for a better understanding. Let’s move on to the current challenges sections.

Before going to the next section, we would like to highlight ourselves here. We are one of the trusted crew of technicians who are dynamically performing the NLP-based projects and researches effectively . As the matter of fact, we are offering so many successful projects all over the world by using the emerging techniques in technology. Now we can have the next section.

Current Challenges in NLP

• Context/Intention Understanding
• Voice Ambiguity/Vagueness
• Data Transformation
• Semantic Context Extracting
• Word Phrase Matching
• Vocabulary/Terminologies Creation
• PoS Tagging & Tokenization

The above listed are the current challenges that get involved in natural language processing. Besides, we can overcome these challenges by improving the NLP model by means of their performance. On the other hand, our technical experts in the concern are usually testing natural language processing approaches to abolish these constraints.

In the following passage, our technical team elaborately explained to you the various natural language processing approaches for the ease of your understanding. In fact, our researchers are always focusing on the students understanding so that they are categorizing each and every edge needed for the NLP-oriented tasks and approaches .  Are you interested to know about that? Now let’s we jump into the section.

Different NLP Approaches

Domain Model-based Approaches

• Loss Centric
• Feature Centric
• Pre-Training
• Pseudo Labeling
• Data Selection
• Model + Data-Centric

Machine Learning-based Approaches

• Association
• K-Means Clustering
• Anomalies Recognition
• Data Parsing
• Regular Emotions/Expressions
• Syntactic Interpretations
• Pattern Matching
• BFS Co-location Data
• BERT & BioBERT
• Decision Trees
• Logistic Regression
• Linear Regression
• Random Forests
• Support Vector Machine
• Gradient-based Networks
• Convolutional Neural Network
• Deep Neural Networks

Text Mining Approaches

• K-nearest Neighbor
• Naïve Bayes
• Predictive Modeling
• Association Rules
• Classification
• Document Indexing
• Term & Inverse Document Frequency
• Document Term Matrix
• Distribution
• Keyword Frequency
• Term Reduction/Compression
• Stemming/lemmatization
• Tokenization
• NLP & Log Parsing
• Text Taxonomies
• Text Classifications
• Text Categorization
• Text Clustering

The above listed are the 3 major approaches that are mainly used for natural languages processing in real-time . However, there are some demerits and merits are presented with the above-listed approaches. It is also important to know about the advantages and disadvantages of the NLP approaches which will help you to focus on the constraints and lead will lead you to the developments. Shall we discuss the pros and cons of NLP approaches? Come on, guys!

Advantages & Disadvantages of NLP Approaches

• Effortless Debugging
• Effective Precisions
• Multi-perspectives
• Short Form Reading
• Ineffective Parsing
• Poor Recalls
• Excessive Skills
• Low Scalability
• Speed Processes
• Resilient Results
• Effective Documentation
• Better Recalls
• High Scalability
• Narrow Understanding
• Poor in Reading Messages
• Huge Annotations
• Complex in Debugging

The foregoing passage conveyed to you the pros and cons of two approaches named machine learning and text mining. The best approach is also having pros and cons. If you do want further explanations or clarifications on that you can feel free to approach our researchers to get benefit from us. Generally, NLP models are trained to perform every task in order to recognize the inputs with latest natural language processing project ideas . Yes, you people guessed right! The next section is all about the training models of the NLP.

Training Models in NLP

• Scratch dataset such as language-specific BERTs & multi-linguistic BERT
• These are the datasets used in model pre-training
• Auxiliary based Pre-Training
• It is the additional data tasks used for labeled adaptive pre-training
• Multi-Phase based Pre-Training
• Domain & broad tasks are the secondary phases of pre-training
• Unlabeled data sources make differences in the multiphase pre-training
• TAPT, DAPT, AdaptaBERT & BioBERT are used datasets

As this article is named as natural language processing thesis topics , here we are going to point out to you the latest thesis topics in NLP for your reference. Commonly, a thesis is the best illustration of the projects or researches done in the determined areas. In fact, they convey the researchers’ perspectives & thoughts to the opponent by the effective structures of the thesis. If you are searching for thesis writing assistance then this is the right platform, you can surely approach our team at any time.

In the following passage, we have itemized some of the latest thesis topics in NLP .  We thought that it would help you a lot. Let’s get into the next section. As this is an important section, you are advised to pay your attention here. Are you really interested in getting into the next section? Come let us also learn them.

Latest Natural Language Processing Thesis Topics

• Cross & Multilingual based NLP Methods
• Multi-modal based NLP Methodologies
• Provocative based NLP Systems
• Graph oriented NLP Techniques
• Data Amplification in NLP
• Reinforcement Learning based NLP
• Dialogue/Voice Assistants
• Market & Customer Behavior Modeling
• Text Classification by Zero-shot/Semi-supervised Learning & Sentiment Analysis
• Text Generation & Summarization
• Relation & Knowledge Extraction for Fine-grained Entity Recognition
• Knowledge & Open-domain based Question & Answering

These are some of the latest thesis topics in NLP . As the matter of fact, we have delivered around 200 to 300 thesis with fruitful outcomes. Actually, they are very innovative and unique by means of their features. Our thesis writing approaches impress the institutes incredibly. At this time, we would like to reveal the future directions of the NLP for the ease of your understanding.

How to select the best thesis topics in NLP?

• See the latest IEEE and other benchmark papers
• Understand the NLP Project ideas recently proposed
• Highlight the problems and gaps
• Get the future scope of each existing work

Come let’s move on to the next section.

Future Research Directions of Natural Language Processing

• Logical Reasoning Chains
• Statistical Integrated Multilingual & Domain Knowledge Processing
• Combination of Interacting Modules

On the whole, NLP requires a better understanding of the texts. In fact, they understand the text’s meaning by relating to the presented word phrases. Conversion of the natural languages in reasoning logic will lead NLP to future directions. By allowing the modules to interact can enhance the NLP pipelines and modules. So far, we have come up with the areas of natural language processing thesis topics and each and every aspect that is needed to do a thesis. If you are in dilemma you could have the valuable opinions of our technical experts.

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NLP is expanded as Natural language processing (NLP). It is a method to support the contextual theory of computational approaches to learning human languages. By the by, it is aimed to implement automated analysis, interpretation of human language in a natural way. We provide 10+ interesting latest NLP Thesis Topics.Let’s check two steps to process the NLP,

• NLP system usually takes a series of words/phrases as input.
• Then, process the input to analyze the meaning and generate structured representation as output. In point of fact, the output nature will differ based on the proposed tasks.

From this page, we gain more meaningful information about Natural Language Processing from different research perspectives!!!

            In order to support you from all the research directions, we have well-equipped resource teams that serve you in both NLP research and development . Further, we also include a writing team to prepare a well-structured Thesis . Here, we have listed out few important services that we provide on the NLP PhD / MS study.

Our Motivations for NLP Thesis Writing

• Evolving Concepts
• Growing NLP Models
• Advanced NLP approaches
• New benchmark datasets
• Programming languages / Frameworks
• NLP Thesis Topics
• And many more
• Provide keen guidance on modern algorithms for solving NLP problems
• Give end-to-end assistance on project development in appropriate friendly tools and resources
• Perform an assessment on experimental results and contribute new findings

General Approach to NLP

In order to provide you best NLP research support , we undergo deep study on new frameworks that are perfect to implement textual data science tasks . Since the framework is most important to make your NLP and text mining operations more efficient. Here, we have given you some high-level approaches that are performed on the majority of NLP projects .

• Data Acquisition
• Data Preprocessing
• Data Investigation
• Model Assessment
• Data Visualization

Our experts are great to suggest you best-fitting frameworks for your project . We ensure you that our proposed frameworks are good to execute all necessary NLP approaches . Our developers are proficient to handle not only these approaches but also other approaches. Even though a framework is iterative, we are capable enough to demonstrate data visualization than a linear process . For instance: the KDD process.

Further, if you need more details about the framework or significant approaches, then connect with us. We are ready to fulfill your needs in a timely manner.

What are models in NLP?

The practice of representing organizational patterns in an excellent way is known as NLP models. Here, we have given you some important NLP models that surely yield accurate results in the implementation phase. All these models are efficient to make the machine learn human instructions and work accordingly. We ensure you that we design NLP models to achieve high performance in system automation.

Which NLP models give the best accuracy?

• DMN and Bidirectional LSTM
• Multichannel CNN
• CRF with Dilated CNN
• Linking with Semi-CRF
• Paragraph Vector
• DP with Manual Characteristics
• K-Max Pooling with DCNN
• CNN-assisted Parsing Features
• Lexicon Infused-Phrase Embedding
• Recursive Neural Tensor Network
• LSTM-based Constituency Tree
• Highway links with Bidirectional LSTM
• Bi-LASTM / Bi-LSTM-CRF along with Word+char Embedding
• Advanced Word Embedding with Tree-LSTM
• Bi-LSTM along with Lexicon+word+char Embedding
• MLP along with Gazetteer+word Embedding

How do I choose a thesis for NLP?

Now, we can see the importance of NLP thesis topics. When you are willing to choose an NLP thesis topic, just think of your interested areas which motivate you to do research in the NLP field. As well, your handpicked thesis topic needs to explicitly showcase your passion for research. Also, make sure that your interest in the topic holds throughout the course of the research journey until thesis submission and acceptance.

In general, you need to choose the thesis topic from the current research areas of NLP . So, it is essential to know the present developments of NLP Projects. For that, you have to refer the recent research articles and magazines. Mainly, focus on the widely known concept to have large reference/resource materials. Also, your handpicked topic needs to be most effective than the existing process which no one has achieved before.

When you are confirmed with your interesting research areas, analyze the existing research gaps. For this purpose, do the survey on reputed research journal papers like springer, IEEE, science direct, emerald, etc. Then, assess the pros and cons of existing techniques used in those related research papers. Next, select the set of possible research issues and choose the optimum one. At last, consult with your mentor or field experts on the feasibility of your research issues in a real-world environment.

Prior to finalizing your handpicked research topic, analyze the future research possibilities and current research limitations. Since the lack of future scope is not meant to choose that topic. As well, more limitations may lead to a lot of difficulties in solving your research issue. Also, it takes more time to complete your research. After considering all these aspects, choose the unsolved questions in your desired NLP research area to find the best solutions from the past historical information.

Next, we can see the most important NLP thesis topics from recent research areas. All these topics have a significant role in creating innovations in the field of natural language processing. In addition to topics, we have also included the primary research issue, solving techniques, and supportive datasets.

Once you contact us, we provide you with guidance on all suitable development requirements. Also, we assure you that our proposed research solutions are really advanced to attain the expected results.

List of Natural Language Processing NLP Thesis Topics

• Use ML approach to grade essay review in an automatic way
• Need feature engineering method
• Linear Regression over Data Features (sentiments, lexical diversity, entities count, etc.)
• Human Graded Scores Dataset
• Use Quora dataset about 400,000 pair questions
• Compute semantic equivalence over Quora questions
• Identify the closet one by the binary value
• Need feature engineering processes
• Support up to sentence-level methods. For instance: parsing
• Naïve Bayes Classifier
• Support Vector Machines
• Quora Datasets
• Predict tags over StackOverflow Q&A using ML approach
• Utilizes conventional multi-label text classification
• For instance: Every query has multiple tags
• Labeled LDA
• Stack Overflow Questions and Tags
• Although spam filter uses the rule-based method for spam SMS, spammers effortlessly detect and break the rules
• ML model is utilized to forecast the spam SMS and retrain data while spammer add new spam term
• Naive Bayes Classifier
• Spam Collection Datasets
• Perform topic modeling by unsupervised algorithm
• Perform clustering by K clusters
• Do the manual process for investigating the cluster
• Latent Semantic Analysis / LDA
• News Headlines Datasets
• Use of conventional named entity extraction method
• Not flexible to extract health entities in medical data
•  Entities may be symptoms, diseases, procedures, medications, disorders, etc.
• Named Entity Recognition (NER)
• Constrained Random Fields
• Informatics for Integrating Biology and the Bedside
• Forecast tweet language by language recognition
• Natural Language Recognition
• Short text language identification
• Construct automated spell checker model using the correction method
• Spelling Checking and Correction
• Contains massive sentences with misspellings
• Main file holds tags like <ERR targ=sister> siter </ERR> where siter refers sister
• Other files hold statistical info like the number of mistakes, etc.
• Datasets comprise a set of misspellings from Wikipedia
• For instance: broad soldiers (soldiers replaced by shoulders)
• Feed collected tweets as input
• Train a model to classify human opinions/emotion in tweets
• Classify / Cluster into neural, negative, and positive
• Deep Random Forest
• Tweets sentiment tagged by humans

In specific, here we have given you some key datasets of language processing, data mining and text mining. All these datasets are globally accepted by many developers to implement NLP projects. As a matter of fact, each dataset has the objective to support a specific set of NLP operations.

There are several commercial and non-commercial datasets for NLP research. We help you to choose the best free download datasets for your project based on project purposes. Since the result of the project is technically based on the handpicked dataset.

Benchmark Datasets for NLP Projects

• Dataset – ~6.5M Entities and ~5.4M Resources
• Categories – 845K Places, 1.6 Persons, 56K Plants, 280K Companies, 5K Disease, 310K Species
• Purpose – Classification and Ontology
• Purpose – Information Retrieval
• Dataset – Semantic Web
• Purpose – Textual Reasoning, Language Understanding, etc.
• Dataset – ~10,900+ News Documents
• Categories – ~20
• Purpose – Clustering and Classification
• Dataset – US Profiling about World Territories and Countries
• Categories – Government, Transportation, etc.
• Purpose – Translation, Processing, and Analysis
• Purpose – Dialogue Models, Speech Collection, Speech Recognition, Speech Synthesis, etc.
• Dataset – ~21575+ Text Documents
• Categories – Group of Categorized Documents
• Purpose – Classification

In addition, we have also given you some important open-source development frameworks and programming languages for NLP projects. When the dataset of the project is confirmed, the next step is to select suitable developing technologies. To choose the optimal one, analyze the supportive libraries, modules, toolboxes, packages, and simplicity of language. Majorly, Core Java and Python are considered as developer-friendly languages which are flexible to develop many sorts of NLP applications/systems.

Programming Languages for NLP

Overall, we are here to provide you best end-to-end research services in Natural Language Processing using python research field. As well, we have an abundant amount of new NLP thesis to make you develop modernistic research work. Also, we suggest suitable development platforms, tools, and technologies based on your project needs. Further, we also provide support in preparing the perfect thesis. Overall, we guarantee you that we meet your level of satisfaction through our smart solutions. So, connect with us to know more Interesting NLP thesis topics to begin your PhD / MS study.

• Natural Language Processing NLP Thesis Topics

Deep Learning for Natural Language Processing: A Survey

• Published: 26 June 2023
• Volume 273 , pages 533–582, ( 2023 )

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• E. O. Arkhangelskaya 1 &
• S. I. Nikolenko 2 , 3  

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Over the last decade, deep learning has revolutionized machine learning. Neural network architectures have become the method of choice for many different applications; in this paper, we survey the applications of deep learning to natural language processing (NLP) problems. We begin by briefly reviewing the basic notions and major architectures of deep learning, including some recent advances that are especially important for NLP. Then we survey distributed representations of words, showing both how word embeddings can be extended to sentences and paragraphs and how words can be broken down further in character-level models. Finally, the main part of the survey deals with various deep architectures that have either arisen specifically for NLP tasks or have become a method of choice for them; the tasks include sentiment analysis, dependency parsing, machine translation, dialog and conversational models, question answering, and other applications.

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Are Deep Learning Approaches Suitable for Natural Language Processing?

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Arkhangelskaya, E.O., Nikolenko, S.I. Deep Learning for Natural Language Processing: A Survey. J Math Sci 273 , 533–582 (2023). https://doi.org/10.1007/s10958-023-06519-6

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Disentanglement

Graph representation learning, sentence embeddings.

Network Embedding

Classification.

Text Classification

Graph Classification

Audio Classification

Medical Image Classification

Language modelling.

Long-range modeling

Protein language model, sentence pair modeling, deep hashing, table retrieval, nlp based person retrival, question answering.

Open-Ended Question Answering

Open-Domain Question Answering

Conversational question answering.

Answer Selection

Translation, image generation.

Image-to-Image Translation

Text-to-Image Generation

Image Inpainting

Conditional Image Generation

Data augmentation.

Image Augmentation

Text Augmentation

Machine translation.

Transliteration

Multimodal Machine Translation

Bilingual lexicon induction.

Unsupervised Machine Translation

Text generation.

Dialogue Generation

Data-to-Text Generation

Multi-Document Summarization

Text style transfer, knowledge graph completion.

Knowledge Graphs

Large language model, triple classification, inductive knowledge graph completion, inductive relation prediction, 2d semantic segmentation, image segmentation.

Scene Parsing

Reflection Removal

Document Classification

Topic Models

Sentence Classification

Emotion Classification

Visual question answering (vqa).

Visual Question Answering

Machine Reading Comprehension

Chart Question Answering

Embodied Question Answering

Named entity recognition (ner).

Nested Named Entity Recognition

Chinese named entity recognition, few-shot ner, sentiment analysis.

Aspect-Based Sentiment Analysis (ABSA)

Multimodal Sentiment Analysis

Aspect Sentiment Triplet Extraction

Twitter Sentiment Analysis

Few-shot learning.

One-Shot Learning

Few-Shot Semantic Segmentation

Cross-domain few-shot.

Unsupervised Few-Shot Learning

Word embeddings.

Learning Word Embeddings

Multilingual Word Embeddings

Embeddings evaluation, contextualised word representations, optical character recognition (ocr).

Active Learning

Handwriting Recognition

Handwritten digit recognition, irregular text recognition, continual learning.

Class Incremental Learning

Continual named entity recognition, unsupervised class-incremental learning, text summarization.

Abstractive Text Summarization

Document summarization, opinion summarization, information retrieval.

Passage Retrieval

Cross-lingual information retrieval, table search, relation extraction.

Relation Classification

Document-level relation extraction, joint entity and relation extraction, temporal relation extraction, link prediction.

Inductive Link Prediction

Dynamic link prediction, hyperedge prediction, anchor link prediction, natural language inference.

Answer Generation

Visual Entailment

Cross-lingual natural language inference, reading comprehension.

Intent Recognition

Implicit relations, active object detection, emotion recognition.

Speech Emotion Recognition

Emotion Recognition in Conversation

Multimodal Emotion Recognition

Emotion-cause pair extraction, natural language understanding, vietnamese social media text processing.

Emotional Dialogue Acts

Image captioning.

3D dense captioning

Controllable image captioning, aesthetic image captioning.

Relational Captioning

Semantic textual similarity.

Paraphrase Identification

Cross-Lingual Semantic Textual Similarity

Event extraction, event causality identification, zero-shot event extraction, dialogue state tracking, task-oriented dialogue systems.

Visual Dialog

Dialogue understanding, coreference resolution, coreference-resolution, cross document coreference resolution, semantic parsing.

AMR Parsing

Semantic dependency parsing, drs parsing, ucca parsing, in-context learning, semantic similarity, conformal prediction.

Text Simplification

Music Source Separation

Decision Making Under Uncertainty

Audio source separation.

Code Generation

Code Translation

Code Documentation Generation

Class-level code generation, library-oriented code generation.

Sentence Embedding

Sentence compression, joint multilingual sentence representations, sentence embeddings for biomedical texts, specificity, dependency parsing.

Transition-Based Dependency Parsing

Prepositional phrase attachment, unsupervised dependency parsing, cross-lingual zero-shot dependency parsing, information extraction, extractive summarization, temporal information extraction, document-level event extraction, cross-lingual, cross-lingual transfer, cross-lingual document classification.

Cross-Lingual Entity Linking

Cross-language text summarization, response generation, common sense reasoning.

Physical Commonsense Reasoning

Riddle sense, anachronisms, memorization, instruction following, visual instruction following, data integration.

Entity Alignment

Entity Resolution

Table annotation, prompt engineering.

Visual Prompting

Entity linking.

Question Generation

Poll generation.

Topic coverage

Dynamic topic modeling, part-of-speech tagging.

Unsupervised Part-Of-Speech Tagging

Mathematical reasoning.

Math Word Problem Solving

Formal logic, geometry problem solving, abstract algebra, abuse detection, hate speech detection, open information extraction.

Hope Speech Detection

Hate speech normalization, hate speech detection crisishatemm benchmark, data mining.

Argument Mining

Opinion Mining

Subgroup discovery, cognitive diagnosis, parallel corpus mining, word sense disambiguation.

Word Sense Induction

Bias detection, selection bias, language identification, dialect identification, native language identification, few-shot relation classification, implicit discourse relation classification, cause-effect relation classification.

Fake News Detection

Relational reasoning.

Semantic Role Labeling

Predicate Detection

Semantic role labeling (predicted predicates).

Textual Analogy Parsing

Slot filling.

Zero-shot Slot Filling

Extracting covid-19 events from twitter, grammatical error correction.

Grammatical Error Detection

Text matching, document text classification, learning with noisy labels, multi-label classification of biomedical texts, political salient issue orientation detection, pos tagging, deep clustering, trajectory clustering, deep nonparametric clustering, nonparametric deep clustering, spoken language understanding, dialogue safety prediction, stance detection, zero-shot stance detection, few-shot stance detection, stance detection (us election 2020 - biden), stance detection (us election 2020 - trump), multi-modal entity alignment, intent detection.

Open Intent Detection

Word similarity, text-to-speech synthesis.

Prosody Prediction

Zero-shot multi-speaker tts, zero-shot cross-lingual transfer, cross-lingual ner, intent classification.

Document AI

Document understanding, fact verification, language acquisition, grounded language learning, entity typing.

Entity Typing on DH-KGs

Constituency parsing.

Constituency Grammar Induction

Self-learning, cross-modal retrieval, image-text matching, multilingual cross-modal retrieval.

Zero-shot Composed Person Retrieval

Cross-modal retrieval on rsitmd, ad-hoc information retrieval, document ranking.

Word Alignment

Model editing, knowledge editing, open-domain dialog, dialogue evaluation, novelty detection, multimodal deep learning, multimodal text and image classification, multi-label text classification.

Text-based Image Editing

Text-guided-image-editing.

Zero-Shot Text-to-Image Generation

Concept alignment, conditional text-to-image synthesis, discourse parsing, discourse segmentation, connective detection.

Shallow Syntax

De-identification, privacy preserving deep learning, sarcasm detection.

Explanation Generation

Morphological analysis.

Session Search

Lemmatization, molecular representation.

Aspect Extraction

Aspect category sentiment analysis, extract aspect.

Aspect-oriented Opinion Extraction

Aspect-Category-Opinion-Sentiment Quadruple Extraction

Chinese Word Segmentation

Handwritten chinese text recognition, chinese spelling error correction, chinese zero pronoun resolution, offline handwritten chinese character recognition, entity disambiguation, conversational search, text-to-video generation, text-to-video editing, subject-driven video generation, source code summarization, method name prediction, speech-to-text translation, simultaneous speech-to-text translation, authorship attribution, text clustering.

Short Text Clustering

Open Intent Discovery

Keyphrase extraction, linguistic acceptability.

Column Type Annotation

Cell entity annotation, columns property annotation, row annotation, abusive language.

Visual Storytelling

KG-to-Text Generation

Unsupervised KG-to-Text Generation

Few-shot text classification, zero-shot out-of-domain detection, term extraction, text2text generation, keyphrase generation, figurative language visualization, sketch-to-text generation, protein folding, phrase grounding, grounded open vocabulary acquisition, deep attention, morphological inflection, multilingual nlp, word translation, spam detection, context-specific spam detection, traditional spam detection, summarization, unsupervised extractive summarization, query-focused summarization.

Natural Language Transduction

Knowledge base population, conversational response selection, cross-lingual word embeddings, passage ranking, text annotation, image-to-text retrieval, key information extraction, biomedical information retrieval.

SpO2 estimation

Authorship verification.

News Classification

Automated essay scoring, graph-to-sequence, keyword extraction, story generation, multimodal association, multimodal generation, sentence summarization, unsupervised sentence summarization, key point matching, component classification, argument pair extraction (ape), claim extraction with stance classification (cesc), claim-evidence pair extraction (cepe), temporal processing, timex normalization, document dating, meme classification, hateful meme classification, morphological tagging, nlg evaluation, entity extraction using gan.

Rumour Detection

Weakly supervised classification, weakly supervised data denoising, semantic composition.

Sentence Ordering

Comment generation.

Lexical Simplification

Token classification, toxic spans detection.

Blackout Poetry Generation

Semantic retrieval, subjectivity analysis.

Emotional Intelligence

Dark humor detection, taxonomy learning, taxonomy expansion, hypernym discovery, conversational response generation.

Personalized and Emotional Conversation

Passage re-ranking, review generation, sentence-pair classification, lexical normalization, pronunciation dictionary creation, negation detection, negation scope resolution, question similarity, medical question pair similarity computation, goal-oriented dialog, user simulation, intent discovery, propaganda detection, propaganda span identification, propaganda technique identification, lexical analysis, lexical complexity prediction, question rewriting, punctuation restoration, reverse dictionary, humor detection.

Legal Reasoning

Meeting summarization, table-based fact verification, attribute value extraction, long-context understanding, pretrained multilingual language models, formality style transfer, semi-supervised formality style transfer, word attribute transfer, diachronic word embeddings, persian sentiment analysis, clinical concept extraction.

Clinical Information Retreival

Constrained clustering.

Only Connect Walls Dataset Task 1 (Grouping)

Incremental constrained clustering, aspect category detection, dialog act classification, extreme summarization.

Hallucination Evaluation

Recognizing emotion cause in conversations.

Causal Emotion Entailment

Nested Mention Recognition

Relationship extraction (distant supervised), binary classification, llm-generated text detection, cancer-no cancer per breast classification, cancer-no cancer per image classification, suspicous (birads 4,5)-no suspicous (birads 1,2,3) per image classification, cancer-no cancer per view classification, clickbait detection, decipherment, semantic entity labeling, text compression, handwriting verification, bangla spelling error correction, ccg supertagging, gender bias detection, linguistic steganography, probing language models, toponym resolution.

Timeline Summarization

Multimodal abstractive text summarization, reader-aware summarization, vietnamese visual question answering, explanatory visual question answering, code repair, thai word segmentation, vietnamese datasets, stock prediction, text-based stock prediction, event-driven trading, pair trading.

Face to Face Translation

Multimodal lexical translation, aggression identification, arabic sentiment analysis, arabic text diacritization, commonsense causal reasoning, fact selection, suggestion mining, temporal relation classification, vietnamese word segmentation, speculation detection, speculation scope resolution, aspect category polarity, complex word identification, cross-lingual bitext mining, morphological disambiguation, scientific document summarization, lay summarization, sign language production, text attribute transfer.

Image-guided Story Ending Generation

Abstract argumentation, dialogue rewriting, logical reasoning reading comprehension.

Multi-agent Integration

Unsupervised sentence compression, stereotypical bias analysis, temporal tagging, anaphora resolution, bridging anaphora resolution.

Abstract Anaphora Resolution

Hope speech detection for english, hope speech detection for malayalam, hope speech detection for tamil, hidden aspect detection, latent aspect detection, personality generation, personality alignment, chinese spell checking, cognate prediction, japanese word segmentation, memex question answering, polyphone disambiguation, spelling correction, table-to-text generation.

KB-to-Language Generation

Text anonymization, vietnamese language models, zero-shot sentiment classification, conditional text generation, contextualized literature-based discovery, multimedia generative script learning, image-sentence alignment, open-world social event classification, action parsing, author attribution, binary condescension detection, conversational web navigation, croatian text diacritization, czech text diacritization, definition modelling, document-level re with incomplete labeling, domain labelling, french text diacritization, hungarian text diacritization, irish text diacritization, latvian text diacritization, misogynistic aggression identification, morpheme segmentaiton, multi-label condescension detection, news annotation, open relation modeling, personality recognition in conversation.

Reading Order Detection

Record linking, role-filler entity extraction, romanian text diacritization, slovak text diacritization, spanish text diacritization, syntax representation, text-to-video search, turkish text diacritization, turning point identification, twitter event detection.

Vietnamese Scene Text

Vietnamese text diacritization, zero-shot machine translation.

Conversational Sentiment Quadruple Extraction

Attribute extraction, legal outcome extraction, automated writing evaluation, chemical indexing, clinical assertion status detection.

Coding Problem Tagging

Collaborative plan acquisition, commonsense reasoning for rl, context query reformulation.

Variable Disambiguation

Cross-lingual text-to-image generation, crowdsourced text aggregation.

Description-guided molecule generation

Multi-modal Dialogue Generation

Page stream segmentation.

Email Thread Summarization

Emergent communications on relations, emotion detection and trigger summarization, extractive tags summarization.

Hate Intensity Prediction

Hate span identification, job prediction, joint entity and relation extraction on scientific data, joint ner and classification, literature mining, math information retrieval, meme captioning, multi-grained named entity recognition, multilingual machine comprehension in english hindi, multimodal text prediction, negation and speculation cue detection, negation and speculation scope resolution, only connect walls dataset task 2 (connections), overlapping mention recognition, paraphrase generation, multilingual paraphrase generation, phrase ranking, phrase tagging, phrase vector embedding, poem meters classification, query wellformedness.

Question-Answer categorization

Readability optimization, reliable intelligence identification, sentence completion, hurtful sentence completion, speaker attribution in german parliamentary debates (germeval 2023, subtask 1), text effects transfer, text-variation, vietnamese aspect-based sentiment analysis, sentiment dependency learning, vietnamese natural language understanding, vietnamese sentiment analysis, vietnamese multimodal sentiment analysis, web page tagging, workflow discovery, incongruity detection, multi-word expression embedding, multi-word expression sememe prediction, trustable and focussed llm generated content, pcl detection, semeval-2022 task 4-1 (binary pcl detection), semeval-2022 task 4-2 (multi-label pcl detection), automatic writing, complaint comment classification, counterspeech detection, extractive text summarization, face selection, job classification, multi-lingual text-to-image generation, multlingual neural machine translation, optical charater recogntion, bangla text detection, question to declarative sentence, relation mention extraction.

Tweet-Reply Sentiment Analysis

Vietnamese parsing.

natural language processing Recently Published Documents

Total documents.

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Towards Developing Uniform Lexicon Based Sorting Algorithm for Three Prominent Indo-Aryan Languages

Three different Indic/Indo-Aryan languages - Bengali, Hindi and Nepali have been explored here in character level to find out similarities and dissimilarities. Having shared the same root, the Sanskrit, Indic languages bear common characteristics. That is why computer and language scientists can take the opportunity to develop common Natural Language Processing (NLP) techniques or algorithms. Bearing the concept in mind, we compare and analyze these three languages character by character. As an application of the hypothesis, we also developed a uniform sorting algorithm in two steps, first for the Bengali and Nepali languages only and then extended it for Hindi in the second step. Our thorough investigation with more than 30,000 words from each language suggests that, the algorithm maintains total accuracy as set by the local language authorities of the respective languages and good efficiency.

Efficient Channel Attention Based Encoder–Decoder Approach for Image Captioning in Hindi

Image captioning refers to the process of generating a textual description that describes objects and activities present in a given image. It connects two fields of artificial intelligence, computer vision, and natural language processing. Computer vision and natural language processing deal with image understanding and language modeling, respectively. In the existing literature, most of the works have been carried out for image captioning in the English language. This article presents a novel method for image captioning in the Hindi language using encoder–decoder based deep learning architecture with efficient channel attention. The key contribution of this work is the deployment of an efficient channel attention mechanism with bahdanau attention and a gated recurrent unit for developing an image captioning model in the Hindi language. Color images usually consist of three channels, namely red, green, and blue. The channel attention mechanism focuses on an image’s important channel while performing the convolution, which is basically to assign higher importance to specific channels over others. The channel attention mechanism has been shown to have great potential for improving the efficiency of deep convolution neural networks (CNNs). The proposed encoder–decoder architecture utilizes the recently introduced ECA-NET CNN to integrate the channel attention mechanism. Hindi is the fourth most spoken language globally, widely spoken in India and South Asia; it is India’s official language. By translating the well-known MSCOCO dataset from English to Hindi, a dataset for image captioning in Hindi is manually created. The efficiency of the proposed method is compared with other baselines in terms of Bilingual Evaluation Understudy (BLEU) scores, and the results obtained illustrate that the method proposed outperforms other baselines. The proposed method has attained improvements of 0.59%, 2.51%, 4.38%, and 3.30% in terms of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores, respectively, with respect to the state-of-the-art. Qualities of the generated captions are further assessed manually in terms of adequacy and fluency to illustrate the proposed method’s efficacy.

Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example

In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques. We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations. Our novel contributions are: The concept of correspondence patterns between metamodels of a transformation. Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML). Symbolic MTBE using “predictive specification” to infer transformations from examples. Transformation generation in multiple MT languages and in Java, from an abstract intermediate language.

A Computational Look at Oral History Archives

Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.

Which environmental features contribute to positive and negative perceptions of urban parks? A cross-cultural comparison using online reviews and Natural Language Processing methods

Natural language processing for smart construction: current status and future directions, attention-based unsupervised keyphrase extraction and phrase graph for covid-19 medical literature retrieval.

Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks—BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms.

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://aka.ms/BLURB .

An ensemble approach for healthcare application and diagnosis using natural language processing

Machine learning and natural language processing enable a data-oriented experimental design approach for producing biochar and hydrochar from biomass, export citation format, share document.

• Schools & departments

Natural Language Processing and Computational Linguistics

A list of potential topics for PhD students in the area of Language Processing.

Concurrency in (computational) linguistics

Improving understanding of synchronic and diachronic aspects of phonology.

Supervisor: Julian Bradfield

In several aspects of linguistic analysis, it is natural to think of some form of concurrent processing, not least because the brain is a massively concurrent system. This is particularly true in phonology and phonetics, and descriptions such as feature analyses, and especially autosegmental phonology, go some way to recognizing this. Although there has been some work on rigorous formal models of such descriptions, there has been little if any application of the extensive body of research in theoretical computer science on concurrent processes. Such a project has the potential to give better linguistic understanding of synchronic and diachronic aspects of phonology and perhaps syntax, and even to improve speech generation and recognition, by adding formal underpinning and improvement to the existing agent-based approaches.

Spectral learning for natural language processing

Supervisors: Shay Cohen, Mirella Lapata

Latent variable modeling is a common technique for improving the expressive power of natural language processing models. The values of these latent variables are missing in the data, but we are still required to predict these values and estimate the model parameters while assuming these variables exist in the model. This project seeks to improve the expressive power of NLP models at various levels (such as morphological, syntactic and semantic) using latent variable modeling, and also to identify key techniques, based on spectral algorithms, in order to learn these models. The family of latent-variable spectral learning algorithms is a recent exciting development that was seeded in the machine learning community. It presents a principled, well-motivated approach for estimating the parameters of latent variable models using tools from linear algebra.

Natural language semantics and question answering

Supervisor: Shay Cohen

How can we make computers understand language? This is a question at the core of the area of semantics in natural language processing. Question answering, an NLP application in which a computer is expected to respond to natural language questions, provides a lens to look into this challenge. Most modern search engines offer some level of functionality for factoid question answering. These systems have high precision, but their recall could significantly be improved. From the technical perspective, question answering offers a sweet spot between challenging semantics problems that are not expected to be solved in the near future, and problems that will be solved in the foreseeable future. As such, it is an excellent test-bed for semantic representation theories and for other attempts at describing the meaning of text. The most recent development in question answering is that of the retrieval of answers from open knowledge bases such as Freebase (a factoid database of various facts without a specific domain tying them all together). The goal of this project is to explore various methods to improve semantic representations in language, with open question answering being potentially an important application for testing them. These semantic representations can either be symbolic (enhanced with a probabilistic interpretation) or they can be projections in a continuous geometric space. Both of these ideas have been recently explored in the literature.

Topics in morphology (NLP or cognitive modelling)

Supevisor:  Sharon Goldwater

Many NLP systems developed for English ignore the morphological structure of words and (mostly) get away with it. Yet morphology is far more important in many other languages. Handling morphology appropriately can reduce sparse data problems in NLP, and understanding human knowledge of morphology is a long-standing scientific question in cognitive science. New methods in both probabilistic modeling and neural networks have the potential to improve word representations for downstream NLP tasks and perhaps to shed light on human morphological acquisition and processing. Projects in this area could involve working to combine distributional syntactic/semantic information with morphological information to improve word representations for low-resource languages or sparse datasets, evaluating new or existing models of morphology against human behavioral benchmarks, or related topics.

Neural Network Models of Human Language and Visual Processing

Supervisor: Frank Keller

Recent neural models have used attention mechanisms as a way of focusing the processing of a neural networks on certain parts of the input. This has proved successful for diverse applications such as image description, question answering, or machine translation. Attention is also a natural way of understanding human cognitive processing: during language processing, humans attend words in a certain order; during visual processing, they view image regions in a certain sequence. Crucially, human attention can be captured precisely using an eye-tracker, a device that measures which parts of the input the eye fixates, and for how long. Projects within this area will leverage neural attention mechanisms to model aspects of human attention. Examples include reading: when reading text, humans systematically skip words, spend more time on difficult words, and sometimes re-read passages. Another example is visual search: when looking for a target, human make a sequence of fixations which depend a diverse range of factors, such as visual salience, scene type, and object context. Neural attention models that capture such behaviors need to combine different types of knowledge, while also offering a cognitively plausible story how such knowledge is acquired, often based on only small amounts of training data.

Neural Network Models of Long-form, Multimodal Narratives

Deep learning approaches are very successful in classical NLP tasks. However, they often assume a limited input context, and are designed to work on short texts. Standard architectures such as LSTMs or transformers therefore struggle to process long-form narratives such as books or screenplays. Prior work shows that such narratives have a particular structure, which can be analyzed in terms of events and characters. This structure can be used for applications such as question answering or summarization of long-form texts. Projects within this area will leverage recent advances in language modeling, such as retrieval-based or memory-based models, to analyze narrative structure. The analysis can take the form of sequences or graphs linking events or characters. Based on such structures, higher level concepts (e.g., schemas or tropes) can be identified, and user reaction such as suspense, surprise, or sentiment can be predicted. Multimodal narratives (illustrated stories, comics, or movies) pose a particular challenge, as narrative elements need to be grounded in both the linguistic and the visual modality to infer structure.

Multi-Sentence Questions

Supervisor: Bonnie Webber

A multi-sentence question (MSQ) is a short text specifying a question or set of related questions. Evidence suggests that the sentences in an MSQ relate to each other in difference ways and that recognizing these relations can enable a system to produce a better response. We have been gathering a corpus of MSQs and beginning to characterize relations within them. Research will involve collecting human responses to MSQs and using them to design and implement a system that produces similar responses.

Concurrent Discourse Relations

Supervisor: Bonnie Webber , Hannah Rohde (LEL)

Evidence from crowd-sourced conjunction-completion experiments shows that people systematically infer implicit discourse relations that hold in addition to discourse relations signalled explicitly. Research on shallow discourse parsing has not yet reflected these findings. It is possible that enabling a shallow discourse parser to recognize implicit relations that hold concurrently with explicitly signalled relations may also help in the recognition of implicit relations without additional signals.  Work in this area could also involve crowd-sourcing additional human judgments on discourse relations.

Low-resource language and speech processing

Supervisors: Sharon Goldwater , Edoardo Ponti

The most effective language and speech processing systems are based on statistical models learned from many annotated examples, a classic application of machine learning on input/ output pairs. But for many languages and domains we have little data. Even in cases where we do have data, it is government or news text. For the vast majority of languages and domains, there is hardly anything. However, in many cases, there is side information that we can exploit: dictionaries or other knowledge sources, or text paired with weak signals, such as images, speech, or timestamps. How can we exploit such heterogeneous information in statistical language processing? The goal of projects in this area is to develop statistical models and inference techniques that exploit such data, and apply them to real problems.

Communicative efficiency approaches to language processing/typology

Supervisor: Frank Mollica

In recent years, communicative efficiency has been formalized in terms of information theory and case studies (e.g., word order patterns, color-naming systems) have been used to demonstrate that linguistic forms and meanings support efficient communication. Despite having communication as a universal objective, the languages of the world are still highly diverse in both their communicative objectives and the strategies they use to achieve efficiency. Projects in this area would involve using information theoretic models and conducting experiments to: identify and characterize communicative functions and grammar strategies; predict and explain the prevalence of communicative functions and grammar strategies across cultures and groups; and investigate the developmental and evolutionary dynamics of grammar.

Incremental interpretation for robust NLP using CCG and dependency parsing

Supervisor: Mark Steedman

Combinatory Categorial Grammar (CCG) is a computational grammar formalism that has recently been used widely in NLP applications including wide-coverage parsing, generation, and semantic parser induction.  The present project seeks to apply insights from these and other sources including dependency parsing to the problem of incremental word-by-word parsing and interpretation using statistical models.  Possible evaluation tasks include language modeling for automatic speech recognition, as well as standard parsing benchmarks.

Data-driven learning of temporal semantics for NLP

Mike Lewis' Edinburgh thesis (2015) shows how to derive a natural language semantics for wide coverage parsers that directly captures relations of paraphrase and entailment using machine learning and parser-based machine reading of large amounts of text.  The present project

seeks to extend the semantics to temporal and causal relations between events, such as that being somewhere is the consequent state of arriving there, using large amounts of timestamped text.

Constructing large knowledge graphs from text using machine reading

Knowledge graphs like Freebase are constructed by hand using relation labels that are not easy to map onto natural language semantics, especially for languages other than English. An obvious alternative is to build the knowledge graph in terms of language-independent natural language semantic relations, which Lewis and Steedman 2013b can be mined by machine reading from multi-lingual text.  The project will investigate the extension of the language independent semantics and its application to the construction of large knowledge resources using parser-based machine reading.

Semantic Parsing for Sequential Question Answering

Supervisor: Mirella Lapata

Semantic parsing maps natural language queries into machine interpretable meaning representations (e.g.,~logical forms or computer programs).  These representations can be executed in a task-specific environment to help users navigate a database, compare products, or reach a decision. Semantic parsers to date can handle queries of varying complexity and a wealth of representations including lambda calculus, dependency-based compositional semantics, variable-free logic, and SQL.

However, the bulk of existing work has focused on isolated queries, ignoring the fact that most natural language interfaces receive inputs in streams. Users typically ask questions or perform tasks in multiple steps, and they often decompose a complex query into a sequence of inter-related sub-queries. The aim of this project is to develop novel neural architectures for training semantic parsers in a context-dependent setting. The task involves simultaneously parsing individual queries correctly and resolving co-reference links between them. An additional challenge involves eliciting datasets which simulate the task of answering sequences of simple but inter-related questions.

Knowledge Graph Completion with Rich Semantics

Supervisor: Jeff Pan

Knowledge Graphs have been shown useful for improving the performance and explain-ability of machine learning methods (such as transfer learning and zero-shot learning) and their downstream tasks, such as NLP tasks.  The present project seeks to investigate how to integrate rich semantics into knowledge graph completion models and methods. Projects in this area could involve integrations of schema of knowledge graph, temporal and spatial constraints, updates of knowledge graphs, or information from language models.

Complex Query Answering over Knowledge Graphs

Answering complex queries on large-scale incomplete knowledge graphs is a fundamental yet challenging task. There have been two extremes in current research: one is to completely relying on logical reasoning of both schema and data sub-graphs but suffers from knowledge incompleteness; the other is to mainly relying embeddings but paying less attention to schema information.  This present project will investigate alternative approaches including those that can make use of schema and embedding effectively. 

Open-Domain Complex Question Answering at Scale

Supervisor: Pasquale Minervini

Open-Domain Question Answering (ODQA) is a task where a system needs to generate the answer to a given general-domain question, and the evidence is not given as input to the system. A core limitation of modern ODQA models is that they remain limited to answering simple, factoid questions, where the answer to the question is explicit in a single piece of evidence. In contrast, complex questions involve aggregating information from multiple documents, requiring some form of logical reasoning and sequential, multi-hop processing in order to generate the answer. Projects in this area involve proposing new ODQA models for answering complex questions, for example, by taking inspiration from models for answering complex queries in Knowledge Graphs (Arakaleyan et al., 2021; Minervini et al., 2022) and Neural Theorem Provers (Minervini et al., 2020a; Minervini et al., 2020b) and proposing methods by which neural ODQA models can learn to search in massively large text corpora, such as the entire Web.

Neuro-Symbolic and Hybrid Discrete-Continuous Natural Language Processing Models

Incorporating discrete components, such as discrete decision steps and symbolic reasoning algorithms, in neural models can significantly improve their interpretability, data efficiency, and predictive properties — for example, see (Niepert et al., 2021; Minervini et al., 2022; Minervini et al., 2020a,b). However, approaches in this space rely either on ad-hoc continuous relaxations (e.g. (Minervini et al., 2020a,b)) or on gradient estimation techniques that require some assumptions on the distributions of the discrete variables (Niepert et al., 2021; Minervini et al., 2022). Projects in this area involve devising neuro-symbolic approaches for solving NLP tasks that require some degree of reasoning and compositionality and identifying gradient estimation techniques (for back-propagating through discrete decision steps) that are both data-efficient, hyper parameter-free, accurate, and require fewer assumptions on the distribution of the discrete variables.

Learning from Graph-Structured Data

Graph-structured data is everywhere – e.g. consider Knowledge Graphs, social networks, protein and drug interaction networks, and molecular profiles. In this project, we aim to improve models for learning from graph-structured data and their evaluation protocols. Projects in this area involve incorporating invariances in graph machine learning models (e.g. see (Minervini et al., 2017)), proposing methods of transferring knowledge between graph representations, automatically identifying functional inductive biases for learning from graphs from a given domain (such as Knowledge Graphs) and proposing techniques for explaining the output of black-box graph machine learning methods (such as graph embeddings).

Modular Transfer Learning

Current neural models often fail to generalise systematically and suffer from negative transfer or catastrophic forgetting across different tasks or languages. A promising solution is endowing neural models with modularity. This property allows for i) disentangling knowledge and recombining it in new, original ways and ii) updating modules locally and asynchronously. Specifically, modules representing different languages / tasks / modalities (such as perception or action) can be implemented as parameter-efficient adapters on top of pre-trained general-purpose language models. The goal of this project is designing modular architectures capable of adapting to new tasks based on few examples.

Next-Generation Tool Synthesis for Complex Information Tasks

Supervisor: Jeff Dalton

The aim of this project is to develop new methods for tool use and plugin-based approaches to allow LLMs to perform complex tasks.  Next-generation virtual assistants based on LLMs interact with external systems to assist users in information tasks, including interacting with search systems and structured API calls. It will develop new models and evaluation methods for “Auto-AppChain'' with human-in-the-loop interaction with evolving scenarios for complex tasks. 

Knowledge Distillation for Adaptive Large Language Models

The aim of this project is to improve the effectiveness of language models to act as knowledge bases and to perform complex reasoning tasks in an environment where information is specialized and rapidly evolving. It will study new methods to encode structured specialized knowledge in language models, how to access the information and develop new methods to edit models to keep them up to date.  This will study how to adapt models to specialized topics and domains effectively while also preserving key capabilities (instruction following, in-context learning, and chat).  

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Dissertations / Theses on the topic 'Natural language processing (Computer science)'

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Naphtal, Rachael (Rachael M. ). "Natural language processing based nutritional application." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100640.

Cosh, Kenneth John. "Supporting organisational semiotics with natural language processing techniques." Thesis, Lancaster University, 2003. http://eprints.lancs.ac.uk/12351/.

張少能 and Siu-nang Bruce Cheung. "A concise framework of natural language processing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31208563.

Lei, Tao Ph D. Massachusetts Institute of Technology. "Interpretable neural models for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108990.

Grinman, Alex J. "Natural language processing on encrypted patient data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113438.

Cheung, Siu-nang Bruce. "A concise framework of natural language processing /." [Hong Kong : University of Hong Kong], 1989. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12432544.

Shepherd, David. "Natural language program analysis combining natural language processing with program analysis to improve software maintenance tools /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 176 p, 2007. http://proquest.umi.com/pqdweb?did=1397920371&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

Bajwa, Imran Sarwar. "A natural language processing approach to generate SBVR and OCL." Thesis, University of Birmingham, 2014. http://etheses.bham.ac.uk//id/eprint/4890/.

Strandberg, Aron, and Patrik Karlström. "Processing Natural Language for the Spotify API : Are sophisticated natural language processing algorithms necessary when processing language in a limited scope?" Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186867.

Bigert, Johnny. "Automatic and unsupervised methods in natural language processing." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156.

Walker, Alden. "Natural language interaction with robots." Diss., Connect to the thesis, 2007. http://hdl.handle.net/10066/1275.

XIAO, MIN. "Generalized Domain Adaptation for Sequence Labeling in Natural Language Processing." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/391382.

Cline, Ben E. "Knowledge intensive natural language generation with revision." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-09092008-063657/.

Chen, Michelle W. M. Eng Massachusetts Institute of Technology. "Comparison of natural language processing algorithms for medical texts." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100298.

Chien, Isabel. "Natural language processing for precision clinical diagnostics and treatment." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119754.

Indovina, Donna Blodgett. "A natural language interface to MS-DOS /." Online version of thesis, 1989. http://hdl.handle.net/1850/10548.

Shah, Aalok Bipin 1977. "Iteractive design and natural language processing in the WISE Project." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80118.

Pham, Son Bao Computer Science &amp Engineering Faculty of Engineering UNSW. "Incremental knowledge acquisition for natural language processing." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/26299.

Li, Wenhui. "Sentiment analysis: Quantitative evaluation of subjective opinions using natural language processing." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/28000.

Jarmasz, Mario. ""Roget's Thesaurus" as a lexical resource for natural language processing." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26493.

Hu, Jin. "Explainable Deep Learning for Natural Language Processing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254886.

O'Sullivan, John J. D. "Teach2Learn : gamifying education to gather training data for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/117320.

Forsyth, Alexander William. "Improving clinical decision making with natural language processing and machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112847.

Manek, Meenakshi. "Natural language interface to a VHDL modeling tool." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-06232009-063212/.

Watanabe, Kiyoshi. "Visible language : repetition and its artistic presentation with the computers." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/17664.

Cohn, Trevor A. "Scaling conditional random fields for natural language processing /." Connect to thesis, 2007. http://eprints.unimelb.edu.au/archive/00002874.

Keller, Thomas Anderson. "Comparison and Fine-Grained Analysis of Sequence Encoders for Natural Language Processing." Thesis, University of California, San Diego, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10599339.

Most machine learning algorithms require a fixed length input to be able to perform commonly desired tasks such as classification, clustering, and regression. For natural language processing, the inherently unbounded and recursive nature of the input poses a unique challenge when deriving such fixed length representations. Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning, the same cannot be said for sequences of words in sentences, paragraphs, or documents. In this work, we study the encoders commonly used to generate fixed length representations of natural language sequences, and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering. Additionally, we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks. Ultimately, we show that the setting in which the encoders are trained, and the corpus used for training, have a greater influence of the final learned representation than the underlying sequence encoders themselves.

Thompson, Cynthia Ann. "Semantic lexicon acquisition for learning natural language interfaces /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

Schäfer, Ulrich. "Integrating deep and shallow natural language processing components : representations and hybrid architectures /." Saarbrücken : German Reseach Center for Artificial Intelligence : Saarland University, Dept. of Computational Linguistics and Phonetics, 2007. http://www.loc.gov/catdir/toc/fy1001/2008384333.html.

Berman, Lucy. "Lewisian Properties and Natural Language Processing: Computational Linguistics from a Philosophical Perspective." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2200.

Huber, Bernard J. Jr. "A knowledge-based approach to understanding natural language. /." Online version of thesis, 1991. http://hdl.handle.net/1850/11053.

Välme, Emma, and Lea Renmarker. "Accelerating Sustainability Report Assessment with Natural Language Processing." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445912.

Linckels, Serge, and Christoph Meinel. "An e-librarian service : natural language interface for an efficient semantic search within multimedia resources." Universität Potsdam, 2005. http://opus.kobv.de/ubp/volltexte/2009/3308/.

Lazic, Marko. "Using Natural Language Processing to extract information from receipt text." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279302.

Chandra, Yohan. "Natural Language Interfaces to Databases." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5474/.

Custy, E. John. "An architecture for the semantic processing of natural language input to a policy workbench." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FCusty.pdf.

Dua, Smrite. "Introducing Semantic Role Labels and Enhancing Dependency Parsing to Compute Politeness in Natural Language." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1430876809.

Dulle, John David. "A caption-based natural-language interface handling descriptive captions for a multimedia database system." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA236533.

Califf, Mary Elaine. "Relational learning techniques for natural language information extraction /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

Ramachandran, Venkateshwaran. "A temporal analysis of natural language narrative text." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03122009-040648/.

Byström, Adam. "From Intent to Code : Using Natural Language Processing." Thesis, Uppsala universitet, Avdelningen för datalogi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325238.

Han, Yo-Sub. "Regular languages and codes /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20HAN.

González, Alejandro. "A Swedish Natural Language Processing Pipeline For Building Knowledge Graphs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254363.

Das, Dipanjan. "Semi-Supervised and Latent-Variable Models of Natural Language Semantics." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/342.

Ramos, Brás Juan Ariel. "Natural language processing and translation using augmented transition networks and semantic networks." Diss., Connect to the thesis, 2003. http://hdl.handle.net/10066/1480.

Kakavandy, Hanna, and John Landeholt. "How natural language processing can be used to improve digital language learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281693.

Mahamood, Saad Ali. "Generating affective natural language for parents of neonatal infants." Thesis, University of Aberdeen, 2010. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=158569.

Augustsson, Christopher. "Multipurpose Case-Based Reasoning System, Using Natural Language Processing." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104890.

Buys, Jan Moolman. "Incremental generative models for syntactic and semantic natural language processing." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:a9a7b5cf-3bb1-4e08-b109-de06bf387d1d.

Botha, Gerrti Reinier. "Text-based language identification for the South African languages." Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-090942008-133715/.

Natural Language Processing

Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more.

Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.

Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.

On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.

Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level.

Recent Publications

Some of our teams.

Impact-Driven Research, Innovation and Moonshots

We're always looking for more talented, passionate people.

The Intelligence Drill Guide to Autonomous Scientific Multimodal NLP Research Directions: A Journey into the Future of Natural Language Processing

As a language enthusiast and AI pioneer, I am constantly fascinated by the evolving landscape of natural language processing (NLP). The field’s ability to unlock the secrets of human communication and bridge the gap between humans and machines has always captivated me.

Today, I want to embark on a journey into the future of NLP with you, exploring a comprehensive guide to multimodal NLP research directions. This guide, meticulously crafted, outlines 220 structured scientific method variants that can propel us towards the next frontier of language processing.

A Scientific Method for NLP Exploration:

The guide introduces the NLP Scientific Method Chain of Thought (CoT), a framework that empowers us to systematically approach NLP research and development. This framework encompasses six key stages:

Observation: Identifying linguistic patterns or phenomena in NLP data.

Question: Formulating critical scientific questions related to the observed linguistic phenomena.

Hypothesis: Proposing testable predictions or educated guesses based on the formulated questions.

Experiment: Designing experiments, linguistic analyses, or model training to gather relevant NLP data.

Analysis: Applying statistical methods to analyze the NLP data and assess the validity of the linguistic hypothesis.

Conclusion: Interpreting the results to determine support or rejection of the NLP hypothesis.

Expanding the Horizons of NLP:

The guide delves into various CoTs, each focusing on a specific aspect of NLP research. These CoTs cover a diverse range of topics, including:

Semantic Analysis CoT: Exploring the nuances of meaning and context in language data.

Sentiment Analysis CoT: Predicting the emotional tone or attitude expressed in textual data.

Multilingual CoT: Investigating language patterns across multiple languages.

Ethical AI CoT: Addressing ethical considerations in language data and AI applications.

Contextual Understanding CoT: Analyzing the impact of context on language interpretation.

Abstractive Summarization CoT: Generating concise and meaningful summaries of large volumes of text.

Named Entity Recognition (NER) CoT: Identifying entities such as names, locations, and organizations in text.

Domain Adaptation CoT: Adapting NLP models to specific domains.

Ambiguity Resolution CoT: Resolving ambiguity in language tasks.

Conversational AI CoT: Building natural and context-aware conversational agents.

Metaphor Analysis CoT: Recognizing and interpreting metaphors in language.

Sarcasm Detection CoT: Identifying sarcastic expressions in textual data.

Idiom Interpretation CoT: Accurately interpreting idiomatic expressions in language.

Ambiguity Resolution in Multi-Lingual Contexts CoT: Resolving ambiguity across multiple languages.

Contextual Anomaly Detection CoT: Identifying and interpreting linguistic anomalies within a given context.

Misinformation Intervention CoT: Developing techniques to identify and mitigate the spread of misinformation.

Empathetic Dialogue Generation CoT: Generating empathetic and emotionally-aware responses in dialogues.

Persona-Driven Conversation CoT: Generating persona-consistent and contextually-appropriate dialogues.

Cognitive Load Optimization in NLP CoT: Optimizing cognitive load and enhancing user experience.

Multimodal Commonsense Reasoning CoT: Leveraging multimodal commonsense knowledge for language understanding.

Emergent Behavior in Multi-Agent NLP Systems CoT: Understanding, controlling, and harnessing emergent behaviors in multi-agent NLP environments.

Adaptive Language Model Fine-Tuning CoT: Optimizing the fine-tuning of language models for new contexts.

Interpretable Explanation Generation CoT: Generating human-understandable explanations for NLP model outputs.

Ethical Bias Mitigation in Text Generation CoT: Mitigating ethical biases in NLP-powered text generation.

Unsupervised Domain Adaptation for NLP CoT: Adapting NLP models to different domains without direct training.

Multilingual Knowledge Transfer CoT: Transferring knowledge and skills across multiple languages.

Generative Adversarial Text Refinement CoT: Improving the quality and coherence of generated text using adversarial training.

Zero-Shot Learning for NLP Tasks CoT: Enabling NLP models to perform tasks without direct training on those specific instances.

Lifelong Language Model Learning CoT: Enabling language models to continuously learn and update their knowledge.

Policy Learning for Ethical Dialogue Agents CoT: Embedding ethical reasoning into conversational AI systems.

Interspecies Communication Language Processing CoT: Understanding and responding to non-human communication signals.

Body Language Processing CoT: Interpreting and responding to non-verbal cues in human communication.

Meta-Analysis and Integration: Reflecting on overarching trends and advancements in NLP, identifying meta-patterns in communication, and exploring the interconnectedness of different NLP domains.

Ethical Considerations and Responsible AI:

As we delve deeper into the world of NLP, it is crucial to address ethical considerations and ensure responsible AI development. The guide emphasizes the importance of:

Mitigating biases and ensuring fairness in NLP algorithms.

Considering the ethical implications of language generation and content moderation.

Enhancing user awareness and consent in NLP applications.

Developing ethical guidelines for NLP development and deployment.

Exploring strategies for fostering responsible AI practices in the global NLP community.

A Collective Journey into the Future:

This guide serves as a valuable resource for researchers, developers, and enthusiasts who are passionate about pushing the boundaries of NLP. By embracing these scientific method variants and prioritizing ethical considerations, we can collectively shape the future of language processing, creating a world where AI and human communication coexist in harmony.

Join me on this exciting journey as we explore the endless possibilities of NLP and unlock the secrets of human language!

Download: 220-Bot.pdf - Google Drive

Marie Seshat Landry

I’m sure you could ask the AI to continue for 220 more random ideas with no value behind them…

Here’s a continuation and completion of the list: Generative Adversarial Text Refinement CoT: Enhancing text quality and coherence through adversarial processes, which involve generating and refining text based on competitive model feedback. Zero-Shot Learning for NLP Tasks CoT: Training models to understand and execute tasks they haven’t been directly trained on, leveraging generalization capabilities. Lifelong Language Model Learning CoT: Developing methods for continual learning, where language models learn from new data over time without forgetting previous knowledge. Policy Learning for Ethical Dialogue Agents CoT: Creating conversational agents that follow ethical guidelines and policies through advanced learning mechanisms. Interspecies Communication Language Processing CoT: Researching methods to decode and respond to communication signals from non-human species. Body Language Processing CoT: Analyzing and interpreting non-verbal human cues, such as gestures and facial expressions, to enhance communication technologies. Meta-Analysis and Integration CoT: Conducting overarching reviews and integrations of NLP research to identify trends, meta-patterns, and the interconnectedness of different NLP domains. …

No value to you does not mean no value to the community. These 220 variants are used to train bots who autonomously devise scientific experiments such as this use case: 220-Bot - War Causes & Analysis by Science.pdf - Google Drive

Perceived value or the lack thereof is irrelevant, forums are for interactions between real people, if folks want to chat with an AI, they’ll go to chatGPT.

@marielandryceo , please refrain from posting AI generated content on our forum in the future.

Glad we could reach an understanding here, I’ll be closing this topic now.

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May 8, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

Using AI to predict grade point average from college application essays

by PNAS Nexus

Jonah Berger and Olivier Toubia used natural language processing to understand what drives academic success. The authors analyzed over 20,000 college application essays from a large public university that attracts students from a range of racial, cultural, and economic backgrounds and found that the semantic volume of the writing, or how much ground an application essay covered predicted college performance, as measured by grade point average.

They published their findings in PNAS Nexus .

Essays that covered more semantic ground predicted higher grades. Similarly, essays with smaller conceptual jumps between successive parts of its discourse predicted higher grades.

These trends held even when researchers controlled for factors including SAT score, parents' education, gender, ethnicity, college major, essay topics, and essay length. Some of these factors, such as parents' education and the student's SAT scores, encode information about family background , suggesting that the linguistic features of semantic volume and speed are not determined solely by socioeconomic status.

According to the authors, the results demonstrate that the topography of thought, or the way people express and organize their ideas, can provide insight into their likely future success.

Journal information: PNAS Nexus

Provided by PNAS Nexus

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