Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Computational Linguistics
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Methods for automatically evaluating answers to complex questions
Information Retrieval
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Multi-perspective question answering using the OpQA corpus
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
Deconstructing nuggets: the stability and reliability of complex question answering evaluation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Open-domain question: answering
Foundations and Trends in Information Retrieval
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Finding the right facts in the crowd: factoid question answering over social media
Proceedings of the 17th international conference on World Wide Web
Semi-Supervised Learning
Using semi-supervised learning for question classification
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Is this urgent?: exploring time-sensitive information needs in collaborative question answering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Using question classification to model user intentions of different levels
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Deconstructing interaction dynamics in knowledge sharing communities
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Proceedings of the 21st international conference companion on World Wide Web
Understanding user intent in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Predicting subjectivity orientation of online forum threads
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Joint question clustering and relevance prediction for open domain non-factoid question answering
Proceedings of the 23rd international conference on World wide web
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An increasingly popular method for finding information online is via the Community Question Answering (CQA) portals such as Yahoo! Answers, Naver, and Baidu Knows. Searching the CQA archives, and ranking, filtering, and evaluating the submitted answers requires intelligent processing of the questions and answers posed by the users. One important task is automatically detecting the question's subjectivity orientation: namely, whether a user is searching for subjective or objective information. Unfortunately, real user questions are often vague, ill-posed, poorly stated. Furthermore, there has been little labeled training data available for real user questions. To address these problems, we present CoCQA, a co-training system that exploits the association between the questions and contributed answers for question analysis tasks. The co-training approach allows CoCQA to use the effectively unlimited amounts of unlabeled data readily available in CQA archives. In this paper we study the effectiveness of CoCQA for the question subjectivity classification task by experimenting over thousands of real users' questions.