Predicting the quality of answers using surface linguistic features

Jung Tae Lee; Young In Song; Hae Chang Rim

doi:10.1109/ALPIT.2007.40

Predicting the quality of answers using surface linguistic features

Jung Tae Lee, Young In Song, Hae Chang Rim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Considering the rapidly increasing mass of information on the Web, the quality of documents is a very critical issue in Web information retrieval. This paper presents the importance of surface linguistic features in predicting the quality of user generated documents. A machine learning approach to incorporating surface linguistic features in predicting of document quality is tested on a collection of answers gathered from a community-driven knowledge search service that allows users to ask and answer questions posed by other users. Experimental results show that the features are useful for predicting the quality of answers.

Original language	English
Title of host publication	Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology
Pages	111-116
Number of pages	6
DOIs	https://doi.org/10.1109/ALPIT.2007.40
Publication status	Published - 2007
Event	6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007 - Luoyang, Henan, China Duration: 2007 Aug 22 → 2007 Aug 24

Publication series

Name	Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology

Other

Other	6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007
Country/Territory	China
City	Luoyang, Henan
Period	07/8/22 → 07/8/24

ASJC Scopus subject areas

General Computer Science
Information Systems

Access to Document

10.1109/ALPIT.2007.40

Cite this

Lee, J. T., Song, Y. I., & Rim, H. C. (2007). Predicting the quality of answers using surface linguistic features. In Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology (pp. 111-116). Article 4460624 (Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology). https://doi.org/10.1109/ALPIT.2007.40

Predicting the quality of answers using surface linguistic features. / Lee, Jung Tae; Song, Young In; Rim, Hae Chang.
Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology. 2007. p. 111-116 4460624 (Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, JT, Song, YI & Rim, HC 2007, Predicting the quality of answers using surface linguistic features. in Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology., 4460624, Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology, pp. 111-116, 6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007, Luoyang, Henan, China, 07/8/22. https://doi.org/10.1109/ALPIT.2007.40

Lee JT, Song YI, Rim HC. Predicting the quality of answers using surface linguistic features. In Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology. 2007. p. 111-116. 4460624. (Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology). doi: 10.1109/ALPIT.2007.40

Lee, Jung Tae ; Song, Young In ; Rim, Hae Chang. / Predicting the quality of answers using surface linguistic features. Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology. 2007. pp. 111-116 (Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology).

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Predicting the quality of answers using surface linguistic features

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