Unsupervised Word Sense Disambiguation for Marathi language using Word Embeddings
Keywords:
Marathi language, Natural Language Processing, Unsupervised Learning, Word Embeddings, Word Sense DisambiguationAbstract
Word Sense Disambiguation (WSD) is a significant challenge within the field of natural language processing. Marathi is considered to be a language with limited resources. Therefore, there has been less research conducted on the Marathi language. There are many methodologies used in Word Sense Disambiguation (WSD), including Knowledge-based and Machine Learning methods. The Lesk algorithm, which uses WordNet as the sense inventory, is well recognized as a prominent technique within the knowledge-based approach for Word Sense Disambiguation (WSD). Word embeddings are a technique used to encode individual words by using low-dimensional vectors with real-valued components. We introduce a method that leverages word embeddings of the terms in a given phrase, except the ambiguous word. Additionally, it incorporates word embeddings of glosses, synonyms, and instances of the ambiguous words obtained from the Marathi WordNet. The most suitable sense of an ambiguous word is then determined using the context and the word embeddings. From the results of our proposed methodology, we could conclude that including the embeddings of synonyms and examples of ambiguous words increases the accuracy of disambiguating words.
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