Unsupervised Approach to Word Sense Disambiguation in Malayalam

Sankar, K. Pramod; Raj, P. C. Reghu; Jayan, V.

doi:10.1016/j.protcy.2016.05.106

Cited by 11 publications

(3 citation statements)

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“…Later, the filtered data was compared with the dictionary file to disambiguate the ambiguous word. P. Iswarya and V Radha [12] introduced an unsupervised learning approach in which part-ofspeech(POS) and clustering techniques were used to handle the homonymy and categorical types of ambiguous words. This approach allows automatic selection of optimal k-value in the k-cluster and construction of sense collocation dictionary.…”

Section: Indian Languagesmentioning

confidence: 99%

“…Also, some proposed systems represent their performance in terms of Precision, Recall and F-Score [15,14,20].In some systems, F1score was also seen using as an evaluation metric, which work best for uneven class distribution [5,8]. Lastly, comparison of a new system with the existing systems was also done to show the effectiveness from the newly developed system [12].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The most obvious application of Word Sense Disambiguation is Machine Translation. WSD also find its applications in many areas such as speech recognition (SR), information extraction (IE) and information retrieval (IR) [12].…”

Section: Introductionmentioning

confidence: 99%

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A Review on Word Sense Disambiguation Emphasizing the Data Resources on Wordnet and Corpus

Singh¹

2021

ITII

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Word Sense Disambiguation is a disambiguating technique of finding the most relevant sense of an ambiguous word with the aid of its surrounding words. In this paper, we pointed out the various Word Sense Disambiguation approaches along with its different techniques, state of the art, comparative studies of the existing system highlighting its benefits and limitations across all the widely well known Indian and foreign languages. In this paper, we converse our study by emphasizing to all the studies that uses WordNet, IndoWordNet or corpus as the main data resources of the referred languages.

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Section: Indian Languagesmentioning

confidence: 99%