Exploring question subjectivity prediction in community QA

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Exploring question subjectivity prediction in community QA

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval table of contents

Singapore, Singapore

POSTER SESSION: Posters group 2: blog, tagging, opinion analysis and web IR table of contents

Pages 735-736

Year of Publication: 2008

ISBN:978-1-60558-164-4

Authors

Baoli Li	Emory University, Atlanta, GA, USA
Yandong Liu	Emory University, Atlanta, GA, USA
Ashwin Ram	Georgia Institute of Technology, Atlanta, GA, USA
Ernest V. Garcia	Emory University, Atlanta, GA, USA
Eugene Agichtein	Emory University, Atlanta, GA, USA

Sponsors

ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

ACM New York, NY, USA

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ABSTRACT

In this paper we begin to investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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