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ÇѱÛÁ¦¸ñ(Korean Title) |
ÅؽºÆ® ½Å·Úµµ ÀÚÁú ±â¹Ý Áö½Ä ÁúÀÇÀÀ´ä ¹®¼ Ç°Áú Æò°¡ ¸ðµ¨ |
¿µ¹®Á¦¸ñ(English Title) |
Text-Confidence Feature Based Quality Evaluation Model for Knowledge Q&A Documents |
ÀúÀÚ(Author) |
ÀÌÁ¤ÅÂ
¼Û¿µÀÎ
¹Ú¼Ò¿µ
ÀÓÇØâ
Jung-Tae Lee
Young-In Song
So-Young Park
Hae-Chang Rim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 35 NO. 10 PP. 0608 ~ 0615 (2008. 10) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
In Knowledge Q&A services where information is created by unspecified users, document quality is an important factor of user satisfaction with search results. Previous work on quality prediction of Knowledge Q&A documents evaluate the quality of documents by using non-textual information, such as click counts and recommendation counts, and focus on enhancing retrieval performance by incorporating the quality measure into retrieval model. Although the non-textual information used in previous work was proven to be useful by experiments, data sparseness problem may occur when predicting the quality of newly created documents with such information. To solve data sparseness problem of non-textual features, this paper proposes new features for document quality prediction, namely text-confidence features, which indicate how trustworthy the content of a document is. The proposed features, extracted directly from the document content, are stable against data sparseness problem, compared to non-textual features that indirectly require participation of service users in order to be collected. Experiments conducted on real world Knowledge Q&A documents suggests that text-confidence features show performance comparable to the non-textual features. We believe the proposed features can be utilized as effective features for document quality prediction and improve the performance of Knowledge Q&A services in the future.
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Å°¿öµå(Keyword) |
Áö½Ä ÁúÀÇÀÀ´ä ¼ºñ½º
¹®¼ Ç°Áú Æò°¡ ¸ðµ¨
½Å·Úµµ ÀÚÁú
Knowledge Q&A service
Document quality prediction model
Text-confidence features
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