scholarly journals Metaheuristic for Word Sense Disambiguation: a Review

2018 ◽  
Vol 7 (3.20) ◽  
pp. 428
Author(s):  
Wafaa AL-Saiagh ◽  
Sabrina Tiun ◽  
Ahmed AL-Saffar ◽  
Suryanti Awang ◽  
A. S. Al-khaleefa
Keyword(s):  
Essential Role ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Word Sense ◽  
Human Language ◽  
Single Word ◽  
Search Results ◽  
Fields Of Study ◽  
Learning Techniques ◽  
Sense Disambiguation

Word Sense Disambiguation (WSD) is the process of determining the exact sense of a particular word in accordance to the context in a computational manner. Such task plays an essential role in multiple fields of study such as Information Retrieval and Information Extraction. With the complexity of human language, WSD came up to solve the problem behind the ambiguity between senses in which a single word would yield different meaning. In this vein, determining the exact meaning of the certain word would facilitate the process of identifying the category of such text, accurate corresponding search results and providing an accurately summarized portion. Several approaches have been proposed for the WSD including statistical, semantic and machine learning techniques. This paper aims to provide a review of such approaches by tackling and categorizing the related works in accordance to the main types. 

A Genetic Fuzzy Semantic Web Search Agent Using Granular Semantic Trees for Ambiguous Queries

2010 ◽  
pp. 426-438
Author(s):  
Yan Chen ◽  
Yan-Qing Zhang
Keyword(s):  
Fuzzy Logic ◽  
Web Search ◽  
Word Sense Disambiguation ◽  
Knowledge Systems ◽  
Web Pages ◽  
Word Sense ◽  
Web Searching ◽  
Search Results ◽  
Sense Disambiguation ◽  
Semantic Tree

For most Web searching applications, queries are commonly ambiguous because words or phrases have different linguistic meanings for different Web users. The conventional keyword-based search engines cannot disambiguate queries to provide relevant results matching Web users’ intents. Traditional Word Sense Disambiguation (WSD) methods use statistic models or ontology-based knowledge systems to measure associations among words. The contexts of queries are used for disambiguation in these methods. However, due to the fact that numerous combinations of words may appear in queries and documents, it is difficult to extract concepts’ relations for all possible combinations. Moreover, queries are usually short, so contexts in queries do not always provide enough information to disambiguate queries. Therefore, the traditional WSD methods are not sufficient to provide accurate search results for ambiguous queries. In this chapter, a new model, Granular Semantic Tree (GST), is introduced for more conveniently representing associations among concepts than the traditional WSD methods. Additionally, users’ preferences are used to provide personalized search results that better adapt to users’ unique intents. Fuzzy logic is used to determine the most appropriate concepts related to queries based on contexts and users’ preferences. Finally, Web pages are analyzed by the GST model. The concepts of pages for the queries are evaluated, and the pages are re-ranked according to similarities of concepts between pages and queries.

Semi-Supervised Word Sense Disambiguation via Context Weighting

2014 ◽  
Vol 1049-1050 ◽  
pp. 1327-1338
Author(s):  
Guo Zhen Zhao ◽  
Wan Li Zuo
Keyword(s):  
Knowledge Acquisition ◽  
Language Processing ◽  
Speech Synthesis ◽  
Large Scale ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Central Research ◽  
Word Sense ◽  
Knowledge Based ◽  
Sense Disambiguation

Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, knowledge-based approach has attracted more and more attention. This paper introduces a new context weighting method, and based on which proposes a novel semi-supervised approach for word sense disambiguation. The significant contribution of our method is that thesaurus and machine learning techniques are integrated in word sense disambiguation. Compared with the state of the art on the test data of the English all words disambiguation task in Sensaval-3, our method yields obvious improvements over existing methods in nouns, adjectives and verbs disambiguation.

scholarly journals Supervised Word Sense Disambiguation with Recurrent Neural Network Model

2019 ◽  
Vol 9 (2) ◽  
pp. 1447-1453
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language ◽  
Language Processing ◽  
Word Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Single Word ◽  
Sense Disambiguation

Disambiguating words is a branch of artificial intelligence that deals with natural language processing. The dissatisfaction of the motive of the word deals with the polysemy of the ambiguous word, processing a single word in natural language, having two or more meanings where the corresponding context discriminates the meaning. Humans are intelligent enough to derive the meaning of the word because they are a biological neural network. Computers can be trained in such a way that they should function similarly to biological neural networks. There are four different suggested approaches to clutter as the knowledge-dependent approach and the machine learning based models which are further classified as supervised, semi-supervised and unpublished learning models. The purpose of this research is to improve better communication between computers and humans. The discussed model used a supervised learning approach with recurrent neural networks.

scholarly journals Multimodal Word Sense Disambiguation in Creative Practice

2020 ◽  
Author(s):  
Manuel Ladron de Guevara ◽  
Christopher George ◽  
Akshat Gupta ◽  
Daragh Byrne ◽  
Ramesh Krishnamurti
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Creative Practice ◽  
Sense Disambiguation
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