A Preliminary Work on Classifying Time Granularities of Temporal Questions

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 finegrained 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.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Wei Li, Wenjie Li, Qin Lu, Kam-Fai Wong
• Keywords: IJCNLP 2005, Natural Language Processing, Temporal Question Answering, Temporal Question Classification, Temporal Questions