TY - GEN
T1 - Building a corpus of temporal-causal structure
AU - Bethard, Steven
AU - Corvey, William
AU - Klingenstein, Sara
AU - Martin, James H.
N1 - Funding Information:
This research was performed under an appointment of the first author to the DHS Scholarship and Fellowship Program, administered by ORISE through an interagency agreement between the U.S. DOE and DHS. ORISE is managed by ORAU under DOE contract number DE-AC05-06OR23100. All opinions expressed in this paper are the author’s and do not necessarily reflect the policies and views of DHS, DOE, or ORAU/ORISE.
PY - 2008
Y1 - 2008
N2 - While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typically considered either temporal relations or causal relations, but not both, and no corpora currently exist that allow the relation between temporals and causals to be examined empirically. We have annotated a corpus of 1000 event pairs for both temporal and causal relations, focusing on a relatively frequent construction in which the events are conjoined by the word and. Temporal relations were annotated using an extension of the BEFORE and AFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on connective phrases like and as a result. The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on causal relations. Analysis of the resulting corpus revealed some interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORE relation. The corpus was also explored using machine learning methods, and while model performance exceeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult temporal and causal relations.
AB - While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typically considered either temporal relations or causal relations, but not both, and no corpora currently exist that allow the relation between temporals and causals to be examined empirically. We have annotated a corpus of 1000 event pairs for both temporal and causal relations, focusing on a relatively frequent construction in which the events are conjoined by the word and. Temporal relations were annotated using an extension of the BEFORE and AFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on connective phrases like and as a result. The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on causal relations. Analysis of the resulting corpus revealed some interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORE relation. The corpus was also explored using machine learning methods, and while model performance exceeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult temporal and causal relations.
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M3 - Conference contribution
AN - SCOPUS:84959893300
T3 - Proceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008
SP - 908
EP - 915
BT - Proceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008
PB - European Language Resources Association (ELRA)
T2 - 6th International Conference on Language Resources and Evaluation, LREC 2008
Y2 - 28 May 2008 through 30 May 2008
ER -