Abstract
This paper describes the named Entity Recognition (NER) System for Hindi using CRF approach. In this paper, our experiments with various feature combinations for Hindi NER have been explained. The training set has been manually annotated with a Named Entity (NE) tagset of 12 tags. The performance of the system has shown improvements by using the part of speech (POS) information of the current and surrounding words, name list, location name list, organization list, person prefix gazetteers list etc. It has been observed that using prefix and suffix feature helped a lot in improving the results. We have achieved Precision, Recall and F-score of 72.78%, 65.82% and 70.45% respectively for the current NER Hindi system. We have used CRF++ toolkit for training and testing data.
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References
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Sharma, R., Goyal, V. (2011). Name Entity Recognition Systems for Hindi Using CRF Approach. In: Singh, C., Singh Lehal, G., Sengupta, J., Sharma, D.V., Goyal, V. (eds) Information Systems for Indian Languages. ICISIL 2011. Communications in Computer and Information Science, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19403-0_5
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DOI: https://doi.org/10.1007/978-3-642-19403-0_5
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