Paper: Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations

ACL ID P08-2045
Title Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

Finding temporal and causal relations is cru- cial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achiev- ing inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal re- lations. We trained machine learning mod- els using features derived from WordNet and the Google N-gram corpus, and they out- performed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. Analysis of these models sug- gests that additional data will improve perfor- mance, and that temporal information is cru- cial to causal relation identification.