You are in:Home/Publications/Mangalore-University@ INLI-FIRE-2017: Indian Native Language Identification using Support Vector Machines and Ensemble Approach

Dr. Hamada Ali Mohamed Ali Nayel :: Publications:

Title:
Mangalore-University@ INLI-FIRE-2017: Indian Native Language Identification using Support Vector Machines and Ensemble Approach
Authors: Hamada A. Nayel; H. L. Shashirekha
Year: 2017
Keywords: Natural Language Processing; Information Retrieval; Native Language Identification; SVM; Ensemble Approach
Journal: Forum for Information Retrieval Evaluation
Volume: 2017
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Hamada Ali Mohamed Ali Nayel_inli2017.pdf
Supplementary materials Not Available
Abstract:

This paper describes the systems submitted by our team for Indian Native Language Identification (INLI) task held in conjunction with FIRE 2017. Native Language Identification (NLI) is an important task that has different applications in different areas such as social-media analysis, authorship identification, second language acquisition and forensic investigation. We submitted two systems using Support Vector Machine (SVM) and Ensemble Classifier based on three different classifiers representing the comments (data) as vector space model for both systems and achieved accuracy of 47.60% and 47.30% respectively and secured second rank over all submissions for the task.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus