A Probability based Classification of Named Entities for Malayalam Language combining Word, Part of Speech and Lexicalized features
Gowri Prasad1, Prakruthi S T2
1Gowri Prasad, Information Science and Engineering, New Horizon College of Engineering, Bengaluru, India.
2Prakruthi S.T, Information Science and Engineering, New Horizon College of Engineering, Bengaluru, India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 839-842 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1968058119/19©BEIESP | DOI: 10.35940/ijrte.A1968.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Named Entity Recognition is the process wherein named entities which are designators of a sentence are identified. Designators of a sentence are domain specific. The proposed system identifies named entities in Malayalam language belonging to tourism domain which generally includes names of persons, places, organizations, dates etc. The system uses word, part of speech and lexicalized features to find the probability of a word belonging to a named entity category and to do the appropriate classification. Probability is calculated based on supervised machine learning using word and part of speech features present in a tagged training corpus and using certain rules applied based on lexicalized features.
Index Terms: Named Entity Recognition, Named Entities, Lexicalized Features, Supervised Machine Learning
Scope of the Article: Machine Learning