Publication Type : Conference Paper
Publisher : CEUR Workshop Proceedings, CEUR-WS.
Source : CEUR Workshop Proceedings, CEUR-WS, Volume 1737, p.197-200 (2016)
Keywords : Artificial intelligence, Classification process, Consumer health information, Fires, Information Retrieval, Keyword-based, Learning systems, Relevant documents, Support vector machines, Three categories, Web users, Word embedding
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2016
Abstract : This work is submitted to Consumer Health Information Search (CHIS) Shared Task in Forum for Information Retrieval Evaluation (FIRE) 2016. Information retrieval from any part of web should include informative content relevant to the search of web user. Hence the major task is to retrieve only relevant documents according to the users query. The given task includes further refinement of the classification process into three categories of relevance such as support, oppose and neutral. Any user reading an article from web must know whether the content of that article supports or opposes title of the article. This seems to be a big challenge to the system. Our proposed system is developed based on the combination of Keyword based features and Word embedding based features. Classification of sentences is done by machine learning based classifier, Support Vector Machine (SVM).
Cite this Research Publication : P. V. Veena, G. Devi, R., Dr. M. Anand Kumar, and Dr. Soman K. P., “AMRITA-CEN@FIRE 2016: Consumer Health Information Search using keyword and word embedding features”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 197-200.