AMRITA _CEN@FIRE 2016: Consumer Health Information Search using Keyword and Word Embedding Features
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)
FIRE