A classification-based approach to question answering in discussion boards. Hong, L. & Davison, B. D. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, of SIGIR '09, pages 171–178, New York, NY, USA, July, 2009. Association for Computing Machinery.
A classification-based approach to question answering in discussion boards [link]Paper  doi  abstract   bibtex   
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.
@inproceedings{Hong_Davison:2009,
	address = {New York, NY, USA},
	series = {{SIGIR} '09},
	title = {A classification-based approach to question answering in discussion boards},
	isbn = {978-1-60558-483-6},
	url = {https://doi.org/10.1145/1571941.1571973},
	doi = {10.1145/1571941.1571973},
	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.},
	urldate = {2023-03-09},
	booktitle = {Proceedings of the 32nd international {ACM} {SIGIR} conference on {Research} and development in information retrieval},
	publisher = {Association for Computing Machinery},
	author = {Hong, Liangjie and Davison, Brian D.},
	month = jul,
	year = {2009},
	keywords = {classification, discussion boards, online forums, question answering},
	pages = {171--178},
}

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