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- W3204289502 abstract "Recently, word sense disambiguation has gained increased attention by NLP practitioner due to its various potential applications in language technology. This paper proposes a Naïve Bayes classifier for resolving lexical ambiguities of Bangla words with the help of a Bangla sense annotated corpus. At the initial stage, a Bangla sense annotated corpus is generated from a raw text corpus for serving as a training dataset. For a given input Bangla sentence, ambiguous words detection is done first and then Bayes probability theorem is applied to calculate the posterior probability that an ambiguous word belongs to a particular sense class. The values of posterior probability of several senses of the detected ambiguous word finally train the Naïve Bayes classifier to classify a closest sense of the ambiguous word. Experimental outcome reveals that the proposed method outdoes existing techniques by achieving the highest F1-score of (90%) on the test data." @default.
- W3204289502 created "2021-10-11" @default.
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- W3204289502 date "2021-09-30" @default.
- W3204289502 modified "2023-10-16" @default.
- W3204289502 title "An Empirical Framework for Bangla Word Sense Disambiguation Using Statistical Approach" @default.
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- W3204289502 doi "https://doi.org/10.1007/978-3-030-82469-3_3" @default.
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