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खुला एक्सेस

अमूर्त

Quantum Neural Network based Parts of Speech Tagger for Hindi

Ravi Narayan , V. P. Singh, S. Chakraverty

The parts of speech disambiguation in corpora is most challenging area in Natural Language Processing. However, some works have been done in the past to overcome the problem of bilingual corpora disambiguation forHindi using Hidden Markov Model and Neural Network. In this paper, Quantum Neural Network (QNN) forHindi parts of speech tagger has been used.To analyze the effectiveness of the proposed approach, 2600 sentences of news items having 11500 words from various newspapers have been evaluated. During simulations and evaluation, the accuracy upto 99.13% is achieved, which is significantly better in comparison with other existing approaches for Hindi parts of speech tagging.