JU_KS_Group@FIRE 2016: Consumer Health Information Search
This work addresses the challenge of improving information retrieval for consumer health queries, but it is incremental as it applies existing methods to a shared task.
The paper tackled the problem of classifying sentences in documents as relevant to queries and further categorizing them as supporting or opposing claims in a consumer health information search task, achieving 73.39% accuracy in the first subtask, ranking third among nine teams.
In this paper, we describe the methodology used and the results obtained by us for completing the tasks given under the shared task on Consumer Health Information Search (CHIS) collocated with the Forum for Information Retrieval Evaluation (FIRE) 2016, ISI Kolkata. The shared task consists of two sub-tasks - (1) task1: given a query and a document/set of documents associated with that query, the task is to classify the sentences in the document as relevant to the query or not and (2) task 2: the relevant sentences need to be further classified as supporting the claim made in the query, or opposing the claim made in the query. We have participated in both the sub-tasks. The percentage accuracy obtained by our developed system for task1 was 73.39 which is third highest among the 9 teams participated in the shared task.