scholarly journals Improvement WSD Dictionary Using Annotated Corpus and Testing it with Simplified Lesk Algorithm

2015 ◽  
Author(s):  
Ahmed H. Aliwy ◽  
Ayad R. Abbas
Keyword(s):  
Lesk Algorithm

scholarly journals Contribution to Semantic Analysis of Arabic Language

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Anis Zouaghi ◽  
Mounir Zrigui ◽  
Georges Antoniadis ◽  
Laroussi Merhbene
Keyword(s):  
Information Retrieval ◽  
Semantic Analysis ◽  
String Matching ◽  
Ambiguous Word ◽  
Arabic Language ◽  
New Approach ◽  
Matching Algorithm ◽  
Ambiguous Words ◽  
Lesk Algorithm ◽  
Context Of Use

We propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We use the measures employed in the domain of information retrieval, Harman, Croft, and Okapi combined to the Lesk algorithm, to assign the correct sense of those proposed.

Ontology Matching using BabelNet Dictionary and Word Sense Disambiguation Algorithms

2017 ◽  
Vol 5 (1) ◽  
pp. 196 ◽  
Author(s):  
Mohamed Biniz ◽  
Rachid El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Similarity Measures ◽  
Ontology Matching ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Lesk Algorithm ◽  
Reference Ontology ◽  
Selection Of

<p>Ontology matching is a discipline that means two things: first, the process of discovering correspondences between two different ontologies, and second is the result of this process, that is to say the expression of correspondences. This discipline is a crucial task to solve problems merging and evolving of heterogeneous ontologies in applications of the Semantic Web. This domain imposes several challenges, among them, the selection of appropriate similarity measures to discover the correspondences. In this article, we are interested to study algorithms that calculate the semantic similarity by using Adapted Lesk algorithm, Wu &amp; Palmer Algorithm, Resnik Algorithm, Leacock and Chodorow Algorithm, and similarity flooding between two ontologies and BabelNet as reference ontology, we implement them, and compared experimentally. Overall, the most effective methods are Wu &amp; Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).</p>

scholarly journals Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Manish Kumar ◽  
Prasenjit Mukherjee ◽  
Manik Hendre ◽  
Manish Godse ◽  
Baisakhi Chakraborty
Keyword(s):  
Word Sense Disambiguation ◽  
Context Information ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Lesk Algorithm

scholarly journals LESK ALGORITHM AND BABELFY SYSTEM FOR DISAMBIGUATION

2019 ◽  
pp. 54-77
Author(s):  
A.A. Gadzhiev ◽  
◽  
A.K. Khmelev ◽  
Keyword(s):  
Lesk Algorithm

scholarly journals SINONIM DAN WORD SENSE DISAMBIGUATION UNTUK MELENGKAPI DETEKTOR PLAGIAT DOKUMEN TUGAS AKHIR

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Devi Dwi Purwanto
Keyword(s):  
Word Sense Disambiguation ◽  
Assessment Method ◽  
Local Similarity ◽  
Word Sense ◽  
Substitution Level ◽  
Third Stage ◽  
Lesk Algorithm ◽  
Sentence Matching ◽  
Carbon Copy

Plagiarism can be categorized into several levels: carbon copy, the addition of words, word substitutions, changing active into passive sentences, and paraphrase. In this research, the detection is only performed by local similarity assessment method. This research is categorized into 3 major processes: preprocessing, candidate determination, and calculation of similarity. In preprocessing, extraction and conversion of a PDF file into XML is performed. Stopword removal and stemming are also performed in this process. For Candidates determination, the process used VSM (Vector Space Model) algorithm using Lucene.NET. It will then calculate the similarity values of the candidates. Similarity values that meet the threshold will be processed in the third stage. The next process is detecting plagiarism at the level of carbon copy. The plagiarism of the substitution level will be determined by finding synonymous with Lesk algorithm and utilizing WordNet as a language dictionary. Lesk notice the words around it, before doing the search process is synonymous with Lesk, performed first sentence extractor. From this experiment, it is concluded that the determination of synonyms using WordNet and Lesk algorithm does not seem to increase its similarity value role. This is due to the difficulty of finding plagiarism by just substituting words. However, plagiarism at the level of carbon copy can be handled with the help of sentence matching.

scholarly journals Word Sense Disambiguation in Hindi Language Using Score Based Modified Lesk Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 939-954
Author(s):  
Praffullit Tripathi ◽  
Prasenjit Mukherjee ◽  
Manik Hendre ◽  
Manish Godse ◽  
Baisakhi Chakraborty
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Lesk Algorithm ◽  
Hindi Language
Sign in / Sign up

Export Citation Format

Share Document