Word Sense Disambiguation (WSD) for Indonesian Homograph Word Meaning Determination by LESK Algorithm Application

Basuki, Setio; Kholimi, Ali Sofyan; Minarno, Agus Eko; Sumadi, Fauzi Dwi Setiawan; Effendy, M. Rizal Arif

doi:10.1109/icts.2019.8850957

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…To decipher the confusing term, the LESK algorithm has been utilized. The result set of the proposed system is in line with the result set of KBBI as well as provides an accuracy of 78.6% for one of the ambiguous words while 62.5 % for two ambiguous words as explained in [15]. The effort of identifying the meaning of a word in a certain situation is known as word sense disambiguation (WSD).…”

Section: Related Workmentioning

confidence: 65%

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XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

Mandal¹,

Mukherjee²,

Chattopadhyay³

et al. 2022

IJACSA

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The digital India program encourages Indian citizens to become conversant with e-services which are primarily English language-based services. However, the vast majority of the Indian population is comfortable with vernacular languages like Bengali, Assamese, Hindi, etc. The rural villagers are not able to interact with the Relational Database Management system in their native language. Therefore, create a system that produces SQL queries from natural language queries in Bengali, containing ambiguous words. This paper proposes a Bengali Query Processor named Extended Bengali language Query Processing System (XBLQPS) to handle queries containing ambiguous words posted to a Healthcare Information database in the electronic domain. The Healthcare Information database contains doctor, hospital and department details in the Bengali language. The proposed system provides support for the Bengalispeaking Indian rural population to efficiently fetch required information from the database. The proposed system extracts the Bengali root word by removing the inflectional part and categorizing them to a specific part of speech (POS) using modified Bengali WordNet. The proposed system uses manually annotated parts of speech detection of a word based on Bengali WordNet. Patterns of noun phrases are generated to detect the correct noun phrase as well as entity and attribute(s). Entity and attributes are used to prepare the semantic table which is utilized to create the Structured Query Language (SQL). The simplified LESK method is utilized to resolve ambiguous Bengali phrases in this query processing system. The accuracy, precision, recall and F1 score of the system is measured as 70%, 74%, 73%, and 73% respectively.

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Section: Related Workmentioning

confidence: 65%

“…Table I shows the results of a comparative investigation of existing systems with the proposed system. 1) The LESK procedure has been used to disambiguate Indonesian Homograph Word referred in [15]. 2) This system provides 78.6% accuracy if one ambiguous word present, and 62.5% accuracy if two ambiguous words present.…”

Section: Related Workmentioning

confidence: 99%

XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

Mandal¹,

Mukherjee²,

Chattopadhyay³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Patil, et al [16]'s algorithm was evaluated on 6 Marathi polysemous words with a total 14 senses and achieved an overall accuracy of 80%. Arabic [40] and Indonesian [15] achieved almost the same accuracy with 0.1 difference, 78% and 79%. The Arabic WSD was tested on 50 polysemous words with 20 context sentences per word.…”

Section: Resultsmentioning

confidence: 98%

“…The sense with the highest score in the combination is allocated as the most suitable sense within that context. In another study [15] used the LESK algorithm for Indonesian and achieved an accuracy of 78.6% for one Indonesian ambiguous word and 62.5% for two ambiguous words.…”

Section: Related Workmentioning

confidence: 99%

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

Moape,

Olugbara,

Ojo

2024

IJACSA

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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.

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Word Sense Disambiguation for Indian Regional Language Using BERT Model

Chandrika¹,

Kallimani²

2022

Smart Innovation, Systems and Technologies

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Word Sense Disambiguation (WSD) for Indonesian Homograph Word Meaning Determination by LESK Algorithm Application

Cited by 7 publications

References 6 publications

XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

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

Word Sense Disambiguation for Indian Regional Language Using BERT Model

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