Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

Kumar, Manish; Mukherjee, Prasenjit; Hendre, Manik; Godse, Manish; Chakraborty, Basabi

doi:10.14569/ijacsa.2020.0110330

Cited by 6 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With three key phases, including document pre-processing and hyperplane computation, it outperforms recent systems, achieving top scores on PAN 2013 and PAN 2014 datasets. Kumar et al (2020) introduce an Adapted Lesk algorithm-based Word Sense Disambiguation (WSD) system, employing a knowledge-based approach with WordNet. The system consists of three units: Input query, Pre-Processing, and WSD classifier.…”

Section: Unsupervised Methodmentioning

confidence: 99%

Enhancing Academic Integrity: An Analysis of Advanced Techniques for Plagiarism Detection using LESK, Word Sense Disambiguation, and SVM

Upadhyay,

Sinha

2024

IJERR

View full text Add to dashboard Cite

Plagiarism is widespread in academia, from ancient literature to modern research, where scholars' work is copied and published without authorization. In the late 90s, researchers explored various methods to detect plagiarism, including Word Sense Disambiguation (WSD), LESK, and Support Vector Machine (SVM). However, these conventional techniques have shown limitations in aligning with contemporary writing styles. This paper proposes an improved LESK algorithm for word sense detection and Improved SVM for feature extraction, addressing the shortcomings of existing methods and offering enhanced accuracy and efficiency in identifying plagiarized content. The study evaluates the proposed system using three datasets from PAN 2012, PAN 2013, and PAN 2014 documents to assess its performance across different types of text plagiarism. Results demonstrate the system's superiority, achieving higher classification accuracy when trained on the Second Dataset. A comprehensive analysis of the feature’s significance in the training database reveals the importance of discriminative sentence similarity. The proposed system contributes to combating academic dishonesty, ensuring the authenticity of digital content in various contexts. Future work will explore cross-lingual plagiarism detection and image duplicity identification using Word Sense Disambiguation techniques. Additionally, efforts will be made to optimize time complexity for faster execution.

show abstract

Section: Unsupervised Methodmentioning

confidence: 99%

Enhancing Academic Integrity: An Analysis of Advanced Techniques for Plagiarism Detection using LESK, Word Sense Disambiguation, and SVM

Upadhyay,

Sinha

2024

IJERR

View full text Add to dashboard Cite

show abstract

“…To disambiguate, the algorithm calculates the overlap between the words in the context and the words in the definitions to determine the appropriate sense. One of the major drawbacks of the Lesk algorithm is its computational complexity, which stems from the exponential growth of comparisons needed for numerous candidate senses associated with polysemous words across various lexical resources [12].…”

Section: Related Workmentioning

confidence: 99%

“…Their model achieved an F1 score of 66%. The study in [12] used the adapted Lesk and trained a classifier responsible for retrieving senses of the target word from Wordnet, assigning a score based on the number of words common between the target gloss and context word gloss.…”

Section: Related Workmentioning

confidence: 99%

Integrating Lesk Algorithm with Cosine Semantic Similarity to Resolve Polysemy for Setswana Language

Moape,

Olugbara,

Ojo

2024

IJACSA

View full text Add to dashboard Cite

Word Sense Disambiguation (WSD) serves as an intermediate task for enhancing text understanding in Natural Language Processing (NLP) applications, including machine translation, information retrieval, and text summarization. Its role is to enhance the effectiveness and efficiency of these applications by ensuring the accurate selection of the appropriate sense for polysemous words in diverse contexts. This task is recognized as an AI-complete problem, indicating its longstanding complexity since the 1950s. One of the earliest proposed solutions to address polysemy in NLP is the Lesk algorithm, which has seen various adaptations by researchers for different languages over the years. This study proposes a simplified, Lesk-based algorithm to resolve polysemy for Setswana. Instead of combinatorial comparisons among candidate senses that Lesk is based on that cause computational complexity, this study models word sense glosses using Bidirectional Encoder Representations from Transformers (BERT) and Cosine similarity measure, which have been proven to achieve optimal performance in WSD. The proposed algorithm was evaluated on Setswana and obtained an accuracy of 86.66 and an error rate of 14.34, surpassing the accuracy of other Lesk-based algorithms for other languages.

show abstract

“…The authors [11] have proposed a system that consists of three units -WSD classifier, pre-processing, and input query. The input query receives an unstructured query from the user while preprocessing unit transforms this query into structured form which is then transformed to the WSD classifier.…”

Section: Related Workmentioning

confidence: 99%

Homonym Detection Using WordNet and Modified Lesk Approach for English Language

D. V. Kodavade

2024

jes

View full text Add to dashboard Cite

Word sense disambiguation (WSD) is a basic and persistent problem that has existed since its inception in the natural language processing (NLP) area. The process of determining the accurate meaning of a word within a specific context is referred to as word sense disambiguation, commonly known as WSD. In NLP, a single word can have two or more meanings, with each meaning being distinguished by its context. This is known as word polysemy. Its applications span a wide range of fields, such as question answering systems, machine translation, information retrieval (IR) etc. Ontology and NLP are still struggling with ambiguity. Homonyms, which are ubiquitous in most languages, are words that have the same spelling but a different meaning. This method's fundamental premise is to select the appropriate sense by comparing a word's context in a sentence to contexts generated from WordNet. The primary goal of this study is to employ WordNet and the Lesk algorithm for WSD. After the algorithm was put into practice and tested on a collection of sentences that included ambiguous words, the synset was able to determine the proper interpretation for most of the sentences. The Lesk algorithm relies on finding the highest number of shared words (maximum overlap) between a word’s context, prepositions and the definitions for its different meanings(glosses). This approach helps in identifying the most accurate interpretation for a given word within a specific context. According to experimental findings, the suggested strategy considerably boosts performance while identifying homonyms.

show abstract

Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

Cited by 6 publications

References 16 publications

Enhancing Academic Integrity: An Analysis of Advanced Techniques for Plagiarism Detection using LESK, Word Sense Disambiguation, and SVM

Enhancing Academic Integrity: An Analysis of Advanced Techniques for Plagiarism Detection using LESK, Word Sense Disambiguation, and SVM

Integrating Lesk Algorithm with Cosine Semantic Similarity to Resolve Polysemy for Setswana Language

Homonym Detection Using WordNet and Modified Lesk Approach for English Language

Contact Info

Product

Resources

About