CONF Habibi_NLDB2015_2015/IDIAP Query Refinement Using Conversational Context: a Method and an Evaluation Resource Habibi, Maryam Popescu-Belis, Andrei Crowdsourcing evaluation Query Refinement Speech-based Information Retrieval EXTERNAL http://publications.idiap.ch/attachments/papers/2015/Habibi_NLDB2015_2015.pdf PUBLIC Proceedings of NLDB 2015 (20th International Conference on Applications of Natural Language to Information Systems) Passau, Germany Lecture Notes in Computer Science 9103 89-102 978-3-319-19581-0; 978-3-319-195 2015 Springer-Verlag Berlin 10.1007/978-3-319-19581-0_7 doi This paper introduces a query refinement method applied to queries asked by users during a meeting or a conversation. Current approaches suffer from poor quality to achieve this goal, but we argue that their performance could be improved by focusing on the local context of the conversation. The proposed technique first represents the local context by extracting keywords from the transcript of the conversation. It then expands the queries with keywords that best represent the topic of the query (e.g. pairs of expansion keywords together with a weight indicating their topical similarity to the query). Moreover, we present a dataset called AREX and an evaluation metric. We compared our query expansion approach with other methods, on topics extracted from the AREX dataset and based on relevance judgments collected in a crowdsourcing experiment. The comparisons indicate the superiority of our method on both manual and ASR transcripts of the AMI Meeting Corpus.