Word Sense Disambiguation in Bahasa Indonesia Using SVM

Faisal, Edi; Nurifan, Farza; Sarno, Riyanarto

doi:10.1109/isemantic.2018.8549824

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…A number of attempts have also been made to address the issue of ambiguity in Indonesian, particularly in the text domain. With regard to wordsense disambiguation, several studies have been conducted (Uliniansyah and Ishizaki, 2006;Faisal et al, 2018;Mahendra et al, 2018). Nevertheless, research on structural ambiguity resolution, especially in speech, is far less common, and there is a notable gap in this area.…”

Section: Related Workmentioning

confidence: 99%

Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian

Widiaputri,

Purwarianti,

Lestari

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Section: Related Workmentioning

confidence: 99%

Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian

Widiaputri,

Purwarianti,

Lestari

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…Contents on Internet are growing rapidly where existing sentences are containing ambiguous words. Removal of ambiguity from sentences that are containing ambiguous words is called word sense disambiguation as in [13]. In global word sense disambiguation, the shotgunWSD is a one of the best algorithm that is unsupervised and knowledge-based.…”

Section: Related Workmentioning

confidence: 99%

Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

Kumar¹,

Mukherjee²,

Hendre³

et al. 2020

IJACSA

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The process of identifying the meaning of a polysemous word correctly from a given context is known as the Word Sense Disambiguation (WSD) in natural language processing (NLP). Adapted Lesk algorithm based system is proposed which makes use of knowledge based approach. This work utilizes WordNet as the knowledge source (lexical database). The proposed system has three units-Input query, Pre-Processing and WSD classifier. Task of input query is to take the inputs sentence (which is an unstructured query) from the user and render it to the pre-processing unit. Pre-processing unit will convert the received unstructured query into a structured query by adding some features such as Part of Speech (POS) tagging, grammatical identification (Subject, Verb, and Object) and this structured query is transferred to the WSD classifier. WSD classifier uniquely identifies the sense of the polysemous word using the context information of the query and the lexical database.

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“…We compared our opinion words polarity from HEOLS with the opinion word polarity from: 1) Opinion Lexicon; 2) the first sense of adjective word SentiWordNet [30] (positive if the SentiWordNet score > 0 and vice versa), we use SentiWordNet because it was used in previous research [31,32]; and 3) same as in point 2 but we add Word Sense Disambiguation (WSD) using Adapted Lesk [33] to improve the performance [34][35][36].…”

Section: Comparisonmentioning

confidence: 99%

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan¹,

Sarno²,

Sungkono³

2019

IJIES

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Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.

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Word Sense Disambiguation in Bahasa Indonesia Using SVM

Cited by 9 publications

References 12 publications

Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian

Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian

Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

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