Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm

2021 ◽  
pp. 1-15
Author(s):  
Aws Hamed Hamad ◽  
Ali Abdulkareem Mahmood ◽  
Saad Adnan Abed ◽  
Xu Ying
Keyword(s):  
Firefly Algorithm ◽  
Search Algorithm ◽  
Word Sense Disambiguation ◽  
Semantic Relatedness ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Benchmark Datasets ◽  
Point Search ◽  
Overlapping Method ◽  
The Right

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.

MODELING WORDNET GLOSSES TO PERFORM WORD SENSE DISAMBIGUATION

2013 ◽  
Vol 22 (02) ◽  
pp. 1350003 ◽  
Author(s):  
KOSTAS FRAGOS
Keyword(s):  
Goodness Of Fit ◽  
Word Sense Disambiguation ◽  
Semantic Relatedness ◽  
Statistical Test ◽  
Word Sense ◽  
Theoretical Assumption ◽  
Actual Distribution ◽  
Sample Data ◽  
Sense Disambiguation ◽  
Formal Mechanism

In this work, we propose a new measure of semantic relatedness between concepts applied in word sense disambiguation. Using the overlaps between WordNet definitions of concepts (glosses) and the so-called goodness of fit statistical test we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts. More concretely, we model WordNet glosses overlaps by making a theoretical assumption about their distribution and then we quantify the discrepancy between the theoretical and actual distribution. This discrepancy is suitably used to measure the relatedness between the input concepts. The experimental results showed very good performance on SensEval-2 lexical sample data for word sense disambiguation.

scholarly journals Harmony Search Algorithm for Word Sense Disambiguation

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0136614 ◽  
Author(s):  
Saad Adnan Abed ◽  
Sabrina Tiun ◽  
Nazlia Omar
Keyword(s):  
Search Algorithm ◽  
Word Sense Disambiguation ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals WSD-GAN: Word Sense Disambiguation Using Generative Adversarial Networks

2019 ◽  
Vol 33 ◽  
pp. 9943-9944 ◽  
Author(s):  
Zijian Hu ◽  
Fuli Luo ◽  
Yutong Tan ◽  
Wenxin Zeng ◽  
Zhifang Sui
Keyword(s):  
Probability Distribution ◽  
Word Sense Disambiguation ◽  
Generative Adversarial Networks ◽  
Word Sense ◽  
Model Based ◽  
Knowledge Based ◽  
Sense Disambiguation ◽  
Supervised Methods ◽  
The Right ◽  
Word Senses

Word Sense Disambiguation (WSD), as a tough task in Natural Language Processing (NLP), aims to identify the correct sense of an ambiguous word in a given context. There are two mainstreams in WSD. Supervised methods mainly utilize labeled context to train a classifier which generates the right probability distribution of word senses. Meanwhile knowledge-based (unsupervised) methods which focus on glosses (word sense definitions) always calculate the similarity of context-gloss pair as score to find out the right word sense. In this paper, we propose a generative adversarial framework WSD-GAN which combines two mainstream methods in WSD. The generative model, based on supervised methods, tries to generate a probability distribution over the word senses. Meanwhile the discriminative model, based on knowledge-based methods, focuses on predicting the relevancy of the context-gloss pairs and identifies the correct pairs over the others. Furthermore, in order to optimize both two models, we leverage policy gradient to enhance the performances of the two models mutually. Our experimental results show that WSD-GAN achieves competitive results on several English all-words WSD datasets.

Semantic Methods for Textual Entailment

2012 ◽  
pp. 479-494
Author(s):  
Andrew Neel ◽  
Max H. Garzon
Keyword(s):  
Question Answering ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
World Knowledge ◽  
New Approach ◽  
Sense Disambiguation ◽  
Textual Entailment ◽  
Benchmark Datasets ◽  
Previous Solution ◽  
Recognizing Textual Entailment

The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, a new approach is taken to apply world knowledge in much the same way as humans, but captured in large semantic graphs such as WordNet. We show that semantic graphs made of synsets and selected relationships between them enable fairly simple methods that provide very competitive performance. First, assuming a solution to word sense disambiguation, we report on the performance of these methods in four basic areas: information retrieval (IR), information extraction (IE), question answering (QA), and multi-document summarization (SUM), as described using benchmark datasets designed to test the entailment problem in the 2006 Recognizing Textual Entailment (RTE-2) challenge. We then show how the same methods yield a solution to word sense disambiguation, which combined with the previous solution, yields a fully automated solution with about the same performance. We then evaluate this solution on two subsequent RTE Challenge datasets. Finally, we evaluate the contribution of WordNet to provide world knowledge. We conclude that the protocol itself works well at solving entailment given a quality source of world knowledge, but WordNet is not able to provide enough information to resolve entailment with this inclusion protocol.

scholarly journals Kannada word sense disambiguation by finding the overlaps between the concepts

2018 ◽  
Vol 7 (2.6) ◽  
pp. 189
Author(s):  
B Manjunatha Kumar ◽  
Dr M.Siddappa ◽  
Dr J.Prakash
Keyword(s):  
Word Sense Disambiguation ◽  
Semantic Relatedness ◽  
Current Method ◽  
Word Sense ◽  
Lexical Database ◽  
Sense Disambiguation ◽  
A Current

We propose three approaches for disambiguating the Kannada word based on an adaptation of dictionary-based Lesk’s word sense disambiguation technique. Instead of making use of the regular dictionary as the repository of glosses, we used Indo – WordNet lexical database as the source of senses.  Here we adopt a current method of measuring semantic relatedness between the concepts of the Kannada words taken from Indo – WordNet. This measure is dependent on identifying and counting the number of common words present between the glosses of a pair of concepts in accordance with Indo – WordNet.

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