Steven Bethard
James H. Martin
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.
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Digital Library
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Index Terms
- Learning semantic links from a corpus of parallel temporal and causal relations
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