The role of context in question answering systems

Authors:
Jimmy Lin;Dennis Quan;Vineet Sinha;Karun Bakshi;David Huynh;Boris Katz;David R. Karger
Affiliations:
MIT AI Laboratory, Cambridge, MA;MIT AI Laboratory, Cambridge, MA;MIT AI Laboratory, Cambridge, MA;MIT AI Laboratory, Cambridge, MA;MIT AI Laboratory, Cambridge, MA;MIT AI Laboratory, Cambridge, MA;MIT AI Laboratory, Cambridge, MA
Venue:
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Year:
2003

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Abstract

Despite recent advances in natural language question an-swering technology, the problem of designing effective user interfaces has been largely unexplored. We conducted a user study to investigate the problem and discovered that overall, users prefer a paragraph-sized chunk of text over just an exact phrase as the answer to their questions. Fur-thermore, users generally prefer answers embedded in con-text, regardless of the perceived reliability of the source documents. When users research a topic, increasing the amount of text returned to users significantly decreases the number of queries that they pose to the system, suggesting that users utilize supporting text to answer related ques-tions. We believe that these results can serve to guide future developments in question answering user interfaces.