scholarly journals An Efficient Method for Biomedical Word Sense Disambiguation Based on Web-Kernel Similarity

2021 ◽  
Vol 16 (4) ◽  
pp. 1-14
Author(s):  
Mohammed Rais ◽  
Mohammed Bekkali ◽  
Abdelmonaime Lachkar
Keyword(s):  
Rough Set Theory ◽  
Word Sense Disambiguation ◽  
Semantic Relatedness ◽  
External Source ◽  
Word Sense ◽  
Term Weighting ◽  
Web Based ◽  
Short Text ◽  
Sense Disambiguation ◽  
Pubmed Search

Searching for the best sense for a polysemous word remains one of the greatest challenges in the representation of biomedical text. To this end, Word Sense Disambiguation (WSD) algorithms mostly rely on an External Source of Knowledge, like a Thesaurus or Ontology, for automatically selecting the proper concept of an ambiguous term in a given Window of Context using semantic similarity and relatedness measures. In this paper, we propose a Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. This measure uses the large volume of documents returned by PubMed Search engine to determine the greater context for a biomedical short text through a new term weighting scheme based on Rough Set Theory (RST). To illustrate the efficiency of our proposed method, we evaluate a WSD algorithm based on this measure on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. The obtained results demonstrate promising improvements.

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 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.

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.

Sign in / Sign up

Export Citation Format

Share Document