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Báo cáo khoa học: "Machine Learning of Temporal Relations"

This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an oversampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions. . | Machine Learning of Temporal Relations Inderjeet Mani Marc Verhagen Ben Wellner Chong Min Lee and James Pustejovsky The MITRE Corporation 202 Burlington Road Bedford MA 01730 USA Department of Linguistics Georgetown University 37 th and O Streets Washington DC 20036 USA Department of Computer Science Brandeis University 415 South St. Waltham MA 02254 USA imani wellner @mitre.org marc jamesp @cs.brandeis.edu cml54@georgetown.edu Abstract This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness we used temporal reasoning as an oversampling method to dramatically expand the amount of training data resulting in predictive accuracy on link labeling as high as 93 using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions. 1 Introduction The growing interest in practical NLP applications such as question-answering and text summarization places increasing demands on the processing of temporal information. In multidocument summarization of news articles it can be useful to know the relative order of events so as to merge and present information from multiple news sources correctly. In questionanswering one would like to be able to ask when an event occurs or what events occurred prior to a particular event. A wealth of prior research by Passoneau 1988 Webber 1988 Hwang and Schubert 1992 Kamp and Reyle 1993 Lascarides and Asher 1993 Hitzeman et al. 1995 Kehler 2000 and others has explored the different knowledge sources used in inferring the temporal ordering of events including temporal adver-bials tense aspect rhetorical relations pragmatic conventions and background knowledge. For example the narrative convention of events being described in the order in which they occur is followed in 1 but overridden by means of a discourse relation