Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .
Enter the email address you signed up with and we'll email you a reset link.
- We're Hiring!
- Help Center
Download Free PDF
A Review of Literature on Word Sense Disambiguation
Artificial intelligence (AI) has been a major research area in the later quarter of 20 th century and is likely to be even more so in the 21 st century. A key part of AI is Word Sense Disambiguation (WSD) which deals with choosing the correct sense of a word in the given text. All human languages have words with multiple meaning and selecting the intended sense is important. This paper briefly describes various methods presently used for WSD and their relative effectiveness. WSD applications currently find application in Information Retrieval, Information Extraction, Automated Answering Machine, Speech Reorganization, Machine Translation among many others. WSD has promise for the future in taking AI to the next higher level. Kywords: Natural Language Processing (NLP), Artificial Intelligence (AI), Word Sense Disambiguation (WSD), Knowledge Based Methods, Supervised/Unsupervised Methods.
Related papers
In present era, Natural Language Processing (NLP) is critical for improving human-machine communication. It is a broad interest to process textual data and gathers valuable and exact information from these texts. NLP compiles the text and sends the data to a computer for further processing. The current state of NLP's mathematical model for proper understanding of word meaning is unclear, and the meaning of words in context is unclear, evoking multiple senses. The spread and improvement of Natural Language Processing applications are being hampered by ambiguity in interpreting the precise meaning of texts such as machine translation (MT), Human-Machine interfaces, and so on. The approach of discovering the correct interpretation of ambiguous word in a given sentence is accepted as Word Sense Disambiguation (WSD).WSD is recognized as being one of natural language processing's more challenging and unsolved problems. Many ambiguities in natural languages are apparent, and researchers are offering to solve the problem in a variety of languages to achieve good disambiguation. These ambiguities must be solved in order to make sense including its texts and advance NLP processing and applications. WSD has a number of NLP applications for which it could be a problem, such as Machine Translation (MT), Information Retrieval (IR), Dialogues, Speech Synthesis (SS), and Question Answering (QA). The effectiveness of many strategies directly applied to WSD, such as Dictionary and Knowledge-based, Supervised, Semi-Supervised and Unsupervised approach, is compared in this study.
International Journal of Recent Technology and Engineering, 2014
Word sense disambiguation is a technique in the field of natural language processing where the main task is to find the correct sense in which a word occurs in a particular context. It is found to be of vital help to applications such as question answering, machine translation, text summarization, text classification, information retrieval etc. This has resulted in excessive interest in approaches based on machine learning which performs classification of word senses automatically. The main motivation behind word sense disambiguation is to allow the users to make ample use of the available technologies because ambiguities present in any language provide great difficulty in the use of information technology as words in human language that occur in a particular context can be interpreted in more than one way depending on the context. In this paper we put forward a survey of supervised, unsupervised and knowledge based approaches and algorithms available in word sense disambiguation (WSD). Index Terms-Machine readable dictionary, Machine translation, Natural language processing, Wordnet, Word sense disambiguation.
Word sense disambiguation is an important and challenging task in natural language processing. Its goal is to find the correct sense in which a word occurs in a sentence or a query when it can have multiple meanings. It is used in various applications of NLP like machine learning, text summarization, information retrieval etc. In this paper, we made a survey of supervised, unsupervised, knowledge based and corpus based approaches of word sense disambiguation. In this paper, study of various word sense disambiguation strategies has been done.
Journal of Information System and Technology Management, 2021
Background: Word Sense Disambiguation (WSD) is known to have a detrimental effect on the precision of information retrieval systems, where WSD is the ability to identify the meanings of words in context. There is a challenge in inference-correct-sensing on ambiguous words. Through many years of research, there have been various solutions to WSD that have been proposed; they have been divided into supervised and knowledge-based unsupervised. Objective: The first objective of this study was to explore the state-of-art of the WSD method with a hybrid method using ontology concepts. Then, with the findings, we may understand which tools are available to build WSD components. The second objective was to determine which method would be the best in giving good performance results of WSD, by analysing how the methods were used to answer specific WSD questions, their production, and how their performance was analysed. Methods: A review of the literature was conducted relating to the performa...
International Journal of Computer Applications, 2015
Abstract Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches.
In natural language processing (NLP), word sense disambiguation (WSD) is an automatic process carried out by a machine to sense the appropriate meaning of a word in a particular context or in a discourse. Natural language is ambiguous, so that many words may be interpreted in multiple methods depending on the context wherein they occur. The computational identification of which means for words in context is known as word sense disambiguation (WSD). In this paper, we will discuss the ambiguity of the words in the languages and the essential measures to deal with the ambiguous words.
Word Sense Disambiguation (WSD) is the method of the correct sense for word in a context. In this paper we have researched the various approaches for WSD: Knowledge based, Supervised, Semi-supervised, Unsupervised methods. This paper has further elaborated on the supervised methods used for WSD. The methods that are compared in this paper are: Decision Trees, Decision Lists, Support Vector Machines, Neural Networks, Naïve Bayes methods, Exemplar learning.
Academia Letters, 2021
Ethiopian Popular Music through Political and Cultural Shifts , 2024
Studia Philosophica Estonia, 2024
Survival Gadget , 2020
Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan, 2019
Cinej Cinema Journal, 2023
UMYU Scientifica
"La Sicilia", 2004
Radiocarbon, 1989
Obstetrics & Gynecology, 2017
Research in Social Stratification and Mobility, 2004
Concurrency and Computation: Practice and Experience, 2017
Related topics
- We're Hiring!
- Help Center
- Find new research papers in:
- Health Sciences
- Earth Sciences
- Cognitive Science
- Mathematics
- Computer Science
- Academia ©2024