posted by organizer: manjira_87 || 17514 views || tracked by 2 users: [display]

CHIS 2016 2016 : CHIS@FIRE 2016: Shared Task on Consumer Health Information Search (CHIS) held in conjunction with FIRE2016, ISI Kolkata


When Dec 8, 2016 - Dec 10, 2016
Where Indian Statistical Institute Kolkata
Submission Deadline TBD
Categories    health care text analytics   information retrieval   NLP   query retrieval

Call For Papers

Call for Participation
CFP in Shared Task on Consumer Health Information Search (CHIS) collocated with the Forum for Information Retrieval Evaluation (FIRE) 2016 on 8th - 10th December 2016, Kolkata
World Wide Web is increasingly being used by consumers as an aid for health decision making and for self-management of chronic illnesses as evidenced by the fact that one in every 20 searches on google is about health. Information access mechanisms for factual health information retrieval have matured considerably, with search engines providing Fact checked Health Knowledge Graph search results to factual health queries. It is pretty straightforward to get an answer to the query “what are the symptoms of Diabetes” from the search engines. However retrieval of relevant multiple perspectives for complex health search queries which do not have a single definitive answer still remains elusive with most of the general purpose search engines. The presence of multiple perspectives with different grades of supporting evidence (which is dynamically changing over time due to the arrival of new research and practice evidence) makes it all the more challenging for a lay searcher.

We use the term “Consumer Health Information Search” (CHIS) to denote such information retrieval search tasks, for which there is “No Single Best Correct Answer”; Instead multiple and diverse perspectives/points of view (which very often are contradictory in nature) are available on the web regarding the queried information. The goal of CHIS track is to research and develop techniques to support users in complex multi-perspective health information queries.
Given a CHIS query, and a document/set of documents associated with that query, the FIRST task is to classify the sentences in the document as relevant to the query or not. The relevant sentences are those from that document, which are useful in providing the answer to the query. The SECOND task is to classify these relevant sentences as supporting the claim made in the query, or opposing the claim made in the query.

Example query: Does daily aspirin therapy prevent heart attack?

S1: “Many medical experts recommend daily aspirin therapy for preventing heart attacks in people of age fifty and above.” [affirmative/Support]

S2: “While aspirin has some role in preventing blood clots, daily aspirin therapy is not for everyone as a primary heart attack prevention method”. [disagreement/Oppose]
First set of Training Data Release: 15th June 2016
Final set of Training Data Release: 1st August 2016 (we are mailing the dataset to registered participants.)
Test Data Release: 1st September 2016
Run Submission Deadline: 15th September 2016
Results Declared: 1st October 2016
Working Notes Due: 15th October 2016
Conference: 8-10 December 2016
Contact email:;
Track Organizers
Shourya Roy, Xerox Research Centre India
Manjira Sinha, Xerox Research Centre India
Sandya Mannarswamy, Xerox Research Centre India

Related Resources

SIGIR 2019   International ACM SIGIR Conference on Research and Development in Information Retrieval
CL-Aff @ AAAI 2019   CL-Aff Happiness Shared Task @ AAAI AffCon : Modeling Affect-in-Action
ACL 2019   57th Annual Meeting of the Association for Computational Linguistics
JAMIA OPEN 2019   JAMIA Open Special Issue: Precision Medicine in the Patient-Centered Era
NAACL-HLT 2019   Annual Conference of the North American Chapter of the Association for Computational Linguistics
DMD 2018   Shared task on Detecting Malicious Domain names (DMD 2018)
ESWC 2019   The 16th Extended Semantic Web Conference
CL-SciSumm 2018   [CfP] CL-SciSumm Shared Task 2018 @SIGIR’2018: The Scientific Summarization Shared Task
RUSSE 2018   A Shared Task on Word Sense Induction and Disambiguation for the Russian Language