Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Bridging the lexical chasm: statistical approaches to answer-finding
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Expertise browser: a quantitative approach to identifying expertise
Proceedings of the 24th International Conference on Software Engineering
Graph-based ranking algorithms for e-mail expertise analysis
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Finding semantically similar questions based on their answers
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Finding experts in community-based question-answering services
Proceedings of the 14th ACM international conference on Information and knowledge management
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A CDD-based formal model for expert finding
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Recommending questions using the mdl-based tree cut model
Proceedings of the 17th international conference on World Wide Web
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Tapping on the potential of q&a community by recommending answer providers
Proceedings of the 17th ACM conference on Information and knowledge management
Probabilistic question recommendation for question answering communities
Proceedings of the 18th international conference on World wide web
Predicting best answerers for new questions in community question answering
WAIM'10 Proceedings of the 11th international conference on Web-age information management
A community question-answering refinement system
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Foundations and Trends in Information Retrieval
Contributor profiles, their dynamics, and their importance in five q&a sites
Proceedings of the 2013 conference on Computer supported cooperative work
Proceedings of the 22nd international conference on World Wide Web
Learning to rank for question routing in community question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Evolutionary optimization for ranking how-to questions based on user-generated contents
Expert Systems with Applications: An International Journal
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Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.