Named entity recognition in Assamese using CRFS and rules

Author

Sharma, Parmanand ; Sharma, U. ; Kalita, Jugal

Author_Institution

Dept. of Comput. Sci. & Eng., Tezpur Univ., Tezpur, India

fYear

2014

fDate

20-22 Oct. 2014

Firstpage

15

Lastpage

18

Abstract

Named Entity Recognition (NER) is an important task in all Natural Language Processing (NLP) applications. It is the process of identifying and classifying the proper noun into classes such as person, location, organization and miscellaneous. Substantial work has been done in English and other European languages, achieving greater accuracy compared to the Indian Languages. Although NER in Indian languages is a difficult and challenging task and suffers from scarcity of resources, such work has started to appear recently. This paper discusses work on NER in Assamese using both Conditional Random Fields and a Rule-Based approach which gives an F-measure of 90-95% accuracy.

Keywords

information retrieval; knowledge based systems; natural language processing; statistical distributions; Assamese language; CRF; NER; NLP; conditional random fields; named entity recognition; natural language processing; rule-based approach; Computer science; Educational institutions; Europe; Hidden Markov models; Natural language processing; Organizations; Support vector machines; AS; Assamese; CRF; HMM; IE; ME; MUC; NE; NER; NLP; POS; QA; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Asian Language Processing (IALP), 2014 International Conference on

Conference_Location

Kuching

Type

conf

DOI

10.1109/IALP.2014.6973498

Filename

6973498