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 on temporal relations and 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 for temporals and for causals. .
