BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Lucene in Action (In Action series)
Lucene in Action (In Action series)
Implementing clarification dialogues in open domain question answering
Natural Language Engineering
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Designing an interactive open-domain question answering system
Natural Language Engineering
Follow-up question handling in the imix and ritel systems: A comparative study
Natural Language Engineering
Exploring topic continuation follow-up questions using machine learning
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Analyzing Interactive QA Dialogues Using Logistic Regression Models
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Qme!: a speech-based question-answering system on mobile devices
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Towards an empirically motivated typology of follow-up questions: the role of dialogue context
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Finite-state models for speech-based search on mobile devices
Natural Language Engineering
Answering contextual questions based on ontologies and question templates
Frontiers of Computer Science in China
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Contextual question answering (QA), in which users' information needs are satisfied through an interactive QA dialogue, has recently attracted more research attention. One challenge of engaging dialogue into QA systems is to determine whether a question is relevant to the previous interaction context. We refer to this task as relevancy recognition. In this paper we propose a data driven approach for the task of relevancy recognition and evaluate it on two data sets: the TREC data and the HandQA data. The results show that we achieve better performance than a previous rule-based algorithm. A detailed evaluation analysis is presented.