A preliminary work on classifying time granularities of temporal questions

Authors:
Wei Li;Wenjie Li;Qin Lu;Kam-Fai Wong
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;Department of Systems Engineering, the Chinese University of Hong Kong, Shatin, Hong Kong
Venue:
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Year:
2005

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Abstract

Temporal question classification assigns time granularities to temporal questions ac-cording to their anticipated answers. It is very important for answer extraction and verification in the literature of temporal question answering. Other than simply distinguishing between "date" and "period", a more fine-grained classification hierarchy scaling down from "millions of years" to "second" is proposed in this paper. Based on it, a SNoW-based classifier, combining user preference, word N-grams, granularity of time expressions, special patterns as well as event types, is built to choose appropriate time granularities for the ambiguous temporal questions, such as When- and How long-like questions. Evaluation on 194 such questions achieves 83.5% accuracy, almost close to manually tagging accuracy 86.2%. Experiments reveal that user preferences make significant contributions to time granularity classification.