Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
An artificial intelligence approach to information retrieval (abstract only)
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
Finding experts in community-based question-answering services
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
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Predictors of answer quality in online Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommending questions using the mdl-based tree cut model
Proceedings of the 17th international conference on World Wide Web
Finding the right facts in the crowd: factoid question answering over social media
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
Exploring question subjectivity prediction in community QA
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A Comparison of Genetic Algorithms for Optimizing Linguistically Informed IR in Question Answering
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Quality-aware collaborative question answering: methods and evaluation
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Questions in, knowledge in?: a study of naver's question answering community
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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 data driven approach to query expansion in question answering
IRQA '08 Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering
Socializing or knowledge sharing?: characterizing social intent in community question answering
Proceedings of the 18th ACM conference on Information and knowledge management
Expert identification in community question answering: exploring question selection bias
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
What Makes a High-Quality User-Generated Answer?
IEEE Internet Computing
What is not in the bag of words for why-qa?
Computational Linguistics
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Elementary bit string mutation landscapes
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Learning to rank for why-question answering
Information Retrieval
Modeling answerer behavior in collaborative question answering systems
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Learning to rank answers to non-factoid questions from web collections
Computational Linguistics
Quadripartite Graph-based Clustering of Questions
ITNG '11 Proceedings of the 2011 Eighth International Conference on Information Technology: New Generations
Early detection of potential experts in question answering communities
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Knowledge and Social Networks in Yahoo! Answers
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Classification of multiple-sentence questions
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
ACM Transactions on Information Systems (TOIS)
Learning from the past: answering new questions with past answers
Proceedings of the 21st international conference on World Wide Web
Exploiting user profile information for answer ranking in cQA
Proceedings of the 21st international conference companion on World Wide Web
Finding expert users in community question answering
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
| Hi-index | 12.05 |
In this work, a new evolutionary model is proposed for ranking answers to non-factoid (how-to) questions in community question-answering platforms. The approach combines evolutionary computation techniques and clustering methods to effectively rate best answers from web-based user-generated contents, so as to generate new rankings of answers. Discovered clusters contain semantically related triplets representing question-answers pairs in terms of subject-verb-object, which is hypothesized to improve the ranking of candidate answers. Experiments were conducted using our evolutionary model and concept clustering operating on large-scale data extracted from Yahoo! Answers. Results show the promise of the approach to effectively discovering semantically similar questions and improving the ranking as compared to state-of-the-art methods.