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A classification-based approach to question answering in discussion boards
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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval table of contents
Boston, MA, USA
SESSION: Question answering table of contents
Pages 171-178  
Year of Publication: 2009
ISBN:978-1-60558-483-6
Authors
Liangjie Hong  Lehigh University, Bethlehem, PA 18015 USA
Brian D. Davison  Lehigh University, Bethlehem, PA 18015 USA
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Discussion boards and online forums are important platforms for people to share information. Users post questions or problems onto discussion boards and rely on others to provide possible solutions and such question-related content sometimes even dominates the whole discussion board. However, to retrieve this kind of information automatically and effectively is still a non-trivial task. In addition, the existence of other types of information (e.g., announcements, plans, elaborations, etc.) makes it difficult to assume that every thread in a discussion board is about a question. We consider the problems of identifying question-related threads and their potential answers as classification tasks. Experimental results across multiple datasets demonstrate that our method can significantly improve the performance in both question detection and answer finding subtasks. We also do a careful comparison of how different types of features contribute to the final result and show that non-content features play a key role in improving overall performance. Finally, we show that a ranking scheme based on our classification approach can yield much better performance than prior published methods.


REFERENCES

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Collaborative Colleagues:
Liangjie Hong: colleagues
Brian D. Davison: colleagues