tailieunhanh - Báo cáo khoa học: "Learning Event Durations from Event Descriptions"

We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance. | Learning Event Durations from Event Descriptions Feng Pan Rutu Mulkar and Jerry R. Hobbs Information Sciences Institute ISI University of Southern California 4676 Admiralty Way Marina del Rey CA 90292 USA pan rutu hobbs @ Abstract We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information considerably outperforming a baseline and approaching human performance. 1 Introduction Consider the sentence from a news article George W. Bush met with Vladimir Putin in Moscow. How long was the meeting Our first reaction to this question might be that we have no idea. But in fact we do have an idea. We know the meeting was longer than 10 seconds and less than a year. How much tighter can we get the bounds to be Most people would say the meeting lasted between an hour and three days. There is much temporal information in text that has hitherto been largely unexploited encoded in the descriptions of events and relying on our knowledge of the range of usual durations of types of events. This paper describes one part of an exploration into how this information can be captured automatically. Specifically we have developed annotation guidelines to minimize discrepant judgments and annotated 58 articles comprising 2288 events we have developed a method for measuring inter-annotator agreement when the judgments are intervals on a scale and we have shown that machine learning techniques applied to the annotated data considerably out perform a baseline and approach human performance. This research is potentially very important in applications in which the time course of events is to be extracted from news. For example whether two events overlap or are in sequence often depends very much on their durations. .

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