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.
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Li, W., Li, W., Lu, Q., Wong, KF. (2005). A Preliminary Work on Classifying Time Granularities of Temporal Questions. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_37
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DOI: https://doi.org/10.1007/11562214_37
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