An HMM Based Named Entity Recognition System for Indian Languages: The JU System at ICON 2013
This work addresses the problem of entity recognition for Indian language speakers, but it is incremental as it applies an existing method to new datasets.
The paper tackled Named Entity Recognition for seven Indian languages using a Hidden Markov Model, achieving F-measures ranging from 0.4003 to 0.8599 across languages.
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.