Publication Type:

Conference Paper

Source:

CEUR Workshop Proceedings, CEUR-WS, Volume 1737, p.304-308 (2016)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006129637&partnerID=40&md5=886016d1c946cccfb06c2f4b23c207e6

Keywords:

Artificial intelligence, Codes (symbols), Cross validation, Data mining, Entity extractions, extraction, Feature based modeling, Feature extraction, Fires, Indian languages, Information Retrieval, Learning algorithms, Learning systems, NAtural language processing, Natural language processing systems, Overall accuracies, Social media, Support vector machines, Word embedding

Abstract:

Social media text holds information regarding various important aspects. Extraction of such information serves as the basis for the most preliminary task in Natural Language Processing called Entity extraction. The work is submitted as a part of Shared task on Code Mix Entity Extraction for Indian Languages(CMEE-IL) at Forum for Information Retrieval Evaluation (FIRE) 2016. Three different methodology is proposed in this paper for the task of entity extraction for code-mix data. Proposed systems include approaches based on the Embedding models and feature based model. Creation of trigram embedding and BIO tag formatting were done during feature extraction. Evaluation of the system is carried out using machine learning based classifier, SVM-Light. Overall accuracy through cross validation has proven that the proposed system is efficient in classifying unknown tokens too

Notes:

cited By 0; Conference of 2016 Forum for Information Retrieval Evaluation, FIRE 2016 ; Conference Date: 7 December 2016 Through 10 December 2016; Conference Code:125007

Cite this Research Publication

R. G. Devi, Veena, P. V., Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA-CEN@FIRE 2016: Code-mix entity extraction for Hindi-English and Tamil-English tweets”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 304-308.

207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
9th
RANK(INDIA):
NIRF 2017
150+
INTERNATIONAL
PARTNERS