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 English-Sinhala NMT BLEU by about 9 points.
An HMM Based Named Entity Recognition System for Indian Languages: The JU System at ICON 2013
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abstract
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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A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
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 English-Sinhala NMT BLEU by about 9 points.