Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Retrieving answers from frequently asked questions pages on the web
Proceedings of the 14th ACM international conference on Information and knowledge management
A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Predictors of answer quality in online Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Predicting information seeker satisfaction in community question answering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A classification-based approach to question answering in discussion boards
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A syntactic tree matching approach to finding similar questions in community-based qa services
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
A skip-chain conditional random field for ranking meeting utterances by importance
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Segmentation of multi-sentence questions: towards effective question retrieval in cQA services
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Evaluating and predicting answer quality in community QA
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Metadata-aware measures for answer summarization in community Question Answering
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning online discussion structures by conditional random fields
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Community question topic categorization via hierarchical kernelized classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We present a novel answer summarization method for community Question Answering services (cQAs) to address the problem of "incomplete answer", i.e., the "best answer" of a complex multi-sentence question misses valuable information that is contained in other answers. In order to automatically generate a novel and non-redundant community answer summary, we segment the complex original multi-sentence question into several sub questions and then propose a general Conditional Random Field (CRF) based answer summary method with group L1 regularization. Various textual and non-textual QA features are explored. Specifically, we explore four different types of contextual factors, namely, the information novelty and non-redundancy modeling for local and non-local sentence interactions under question segmentation. To further unleash the potential of the abundant cQA features, we introduce the group L1 regularization for feature learning. Experimental results on a Yahoo! Answers dataset show that our proposed method significantly outperforms state-of-the-art methods on cQA summarization task.