Finding similar questions in large question and answer archives
Proceedings of the 14th ACM international conference on Information and knowledge management
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Ranking community answers by modeling question-answer relationships via analogical reasoning
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Understanding and summarizing answers in community-based question answering services
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Model for Voter Scoring and Best Answer Selection in Community Q&A Services
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Semantic chunk annotation for complex questions using conditional random field
KRAQ '08 Coling 2008: Proceedings of the workshop on Knowledge and Reasoning for Answering Questions
Proceedings of the 19th international conference on World wide web
Summarizing Similar Questions for Chinese Community Question Answering Portals
ITCS '10 Proceedings of the 2010 Second International Conference on Information Technology and Computer Science
Metadata-aware measures for answer summarization in community Question Answering
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A graph-based semi-supervised learning for question semantic labeling
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
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The accumulated question-answer archives in community-based question answering (CQA) services have produced a valuable repository for knowledge discovery. The yes-no question is a common type of question which has not been well studied in previous work. This paper proposed a novel approach to analyze answers to Chinese yes-no questions in CQA services. The analysis task was innovatively formed as a sentence selection problem: sentences that best expressed the answerer's opinion were selected. First, a conditional random field (CRF) based annotation model was proposed to split the question into several segments. Then a new score function, which combined position information of the sentence and segmentation information of the question, was designed to score all sentences of one answer. Experiment results validated the effectiveness of the CRF-based question segmentation method. The proposed answer scoring function was also proved to be more appropriate than other approaches with respect to satisfying user's information need.