Query Refinement Using Conversational Context: A Method and an Evaluation Resource

This paper introduces a query refinement method applied to queries asked by users during a meeting or a conversation. The proposed method does not require further clarifications from users, to avoid distracting them from their conversation, but leverages instead the local context of the conversation. The method 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, i.e. expansion keywords accompanied by weights indicating their topical similarity to the query. Moreover, we present a dataset called AREX and an evaluation metric based on relevance judgments collected in a crowdsourcing experiment. We compare our query expansion approach with other methods, over queries extracted from the AREX dataset, showing the superiority of our method when either manual or automatic transcripts of the AMI Meeting Corpus are used.


Editor(s):
Biemann, C
Handschuh, S
Freitas, A
Meziane, F
Metais, E
Published in:
Natural Language Processing And Information Systems, Nldb 2015, 9103, 89-102
Presented at:
20th International Conference on Applications of Natural Language to Information Systems (NLDB), Univ Passau, Passau, GERMANY, JUN 17-19, 2015
Year:
2015
Publisher:
Berlin, Springer-Verlag Berlin
ISSN:
0302-9743
ISBN:
978-3-319-19581-0
978-3-319-19580-3
Keywords:
Laboratories:




 Record created 2016-02-16, last modified 2018-05-09


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)