Shallow Parsing and Word Sense Disambiguation Used for Machine Translation from Hindi to English Languages

Abstract

This paper developed innovative algorithms such as shallow parsing and modified Lesk's algorithm to resolve the issues in Word Sense Disambiguation and performed correct translation from Hindi language to English language. Shallow parsing method is based on Hidden Markov model. We also perform an evaluation for 1657 Hindi tokens with 990 phrases for Parts of speech tagging and Chunking for given Hindi sentence as input and able to achieve Precision, Recall, F-score, Accuracy for Parts of speech tagger: Accuracy: 92.09%; precision: 84.76%; recall: 89.29%; F-score: 86.97, system accuracy for Chunk: Accuracy: 93.96%; precision: 89.33%; recall: 91.31%; F-score: 90.315%. The evaluation is performed by developing confusion matrix in which the system result of Parts of speech tagger and Chunk is compared with Gold standard date provided by IIIT Hyderabad in the summer school 2015. In this paper we discuss the second problem Word Sense Disambiguation in which we enhance the Modified Lesk algorithms by using overlap based method which will find information between three pieces of words in a given context. The system generated result resolves the issues of Word Sense Disambiguation and shows the comparison result with the website Google Translator in which we input polysemy word in a Hindi sentence and same sentence input in our generated system and shows the comparison result of both. The output result shows that our system resolves Word Sense Disambiguation and produces correct translation and Google Translator is fails to resolves the correct Translation.

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