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Báo cáo khoa học: "Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations"

Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus, and they outperformed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. . | Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations Steven Bethard Institute for Cognitive Science Department of Computer Science University of Colorado Boulder CO 80309 USA steven.bethard@colorado.edu James H. Martin Institute for Cognitive Science Department of Computer Science University of Colorado Boulder CO 80309 USA james.martin@colorado.edu Abstract Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations we annotated 1000 conjoined event pairs achieving inter-annotator agreement of 81.2 on temporal relations and 77.8 on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus and they outperformed a variety of baselines achieving an F-measure of 49.0 for temporals and 52.4 for causals. Analysis of these models suggests that additional data will improve performance and that temporal information is crucial to causal relation identification. 1 Introduction Working out how events are tied together temporally and causally is a crucial component for successful natural language understanding. Consider the text 1 I ate a bad tuna sandwich got food poisoning and had to have a shot in my shoulder. wsj-0409 To understand the semantic structure here a system must order events along a timeline recognizing that getting food poisoning occurred BEFORE having a shot. The system must also identify when an event is not independent of the surrounding events e.g. got food poisoning was CAUSED by eating a bad sandwich. Recognizing these temporal and causal relations is crucial for applications like question answering which must face queries like How did he get food poisoning or What was the treatment Currently no existing resource has all the necessary pieces for investigating parallel temporal and causal phenomena. The TimeBank Pustejovsky et al. 2003 links events with .