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An HMM Based Named Entity Recognition System for Indian Languages: The JU System at ICON 2013

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arxiv 1405.7397 v1 pith:GZA5LFDM submitted 2014-05-28 cs.CL

Vivekananda Gayen , Kamal Sarkar This is my paper · ORCID
classification cs.CL
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This paper reports about our work in the ICON 2013 NLP TOOLS CONTEST on Named Entity Recognition. We submitted runs for Bengali, English, Hindi, Marathi, Punjabi, Tamil and Telugu. A statistical HMM (Hidden Markov Models) based model has been used to implement our system. The system has been trained and tested on the NLP TOOLS CONTEST: ICON 2013 datasets. Our system obtains F-measures of 0.8599, 0.7704, 0.7520, 0.4289, 0.5455, 0.4466, and 0.4003 for Bengali, English, Hindi, Marathi, Punjabi, Tamil and Telugu respectively.

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  1. A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala

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    A new manually annotated English-Tamil-Sinhala parallel NER corpus of 3,835 sentences per language, with benchmarks showing XLM-R outperforms monolingual and Indic models, and a case study where NER output improves En...

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