Work

CU-TMP

Year: 2007

Type: conference-paper

Abstract: We approached the temporal relation identification tasks of TempEval 2007 as pair-wise classification tasks. We introduced a variety of syntactically and semantically motivated features, including temporal-logic-based features derived from running our Task B system on the Task A and C data. We trained support vector machine models and achieved the second highest accuracies on the tasks: 61% on Task A, 75% on Task B and 54% on Task C.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.3115/1621474.1621499

Authors: Steven Bethard, James H. Martin

Institution: University of Colorado Boulder

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: U.S. Department of Energy, Oak Ridge Associated Universities, Oak Ridge Institute for Science and Education