An architecture for more realistic conversational systems
Proceedings of the 6th international conference on Intelligent user interfaces
Outstanding Issues in Anaphora Resolution (Invited Talk)
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Discourse processing for context question answering based on linguistic knowledge
Knowledge-Based Systems
Ontology learning: state of the art and open issues
Information Technology and Management
A framework of a logic-based question-answering system for the medical domain (LOQAS-Med)
Proceedings of the 2009 ACM symposium on Applied Computing
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HLT-Demonstrations '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session
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ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Qme!: a speech-based question-answering system on mobile devices
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Journal of Information Science
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Communications of the ACM
Health conversational system based on contextual matching of community-driven question-answer pairs
Proceedings of the 20th ACM international conference on Information and knowledge management
Mining wikipedia and yahoo! answers for question expansion in opinion QA
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Studies have shown that natural language interfaces such as question answering and conversational systems allow information to be accessed and understood more easily by users who are unfamiliar with the nuances of the delivery mechanisms (e.g., keyword-based search engines) or have limited literacy in certain domains (e.g., unable to comprehend health-related content due to terminology barrier). In particular, the increasing use of the web for health information prompts us to reexamine our existing delivery mechanisms. We present enquireMe, which is a contextual question answering system that provides lay users with the ability to obtain responses about a wide range of health topics by vaguely expressing at the start and gradually refining their information needs over the course of an interaction session using natural language. enquireMe allows the users to engage in “conversations” about their health concerns, a process that can be therapeutic in itself. The system uses community-driven question–answer pairs from the web together with a decay model to deliver the top scoring answers as responses to the users' unrestricted inputs. We evaluated enquireMe using benchmark data from WebMD and TREC to assess the accuracy of system-generated answers. Despite the absence of complex knowledge acquisition and deep language processing, enquireMe is comparable to the state-of-the-art question answering systems such as START as well as those interactive systems from TREC. © 2012 Wiley Periodicals, Inc.