| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 713407 | IFAC Proceedings Volumes | 2014 | 6 Pages |
Abstract
The parts of speech disambiguation of corpora is most challenging task in Natural Language Processing (NLP). However, some works have been done in the past to overcome the problem of bilingual corpora disambiguation for Hindi. In this paper, Artificial Neural Network for Hindi 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 up to 91.30% is achieved, which is significantly better in comparison to other existing approaches for Hindi parts of speech tagging.
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