Paper: Recognizing Textual Relatedness with Predicate-Argument Structures

ACL ID D09-1082
Title Recognizing Textual Relatedness with Predicate-Argument Structures
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors
  • Rui Wang (Saarland University, Saarbrucken Germany)
  • Yi Zhang (Saarland University, Saarbrucken Germany; German Research Center for Artificial Intelligence, Saarbrucken Germany)

In this paper, we first compare several strategies to handle the newly proposed three-way Recognizing Textual Entailment (RTE) task. Then we define a new mea- surement for a pair of texts, called Textual Relatedness, which is a weaker concept than semantic similarity or paraphrase. We show that an alignment model based on the predicate-argument structures using this measurement can help an RTE system to recognize the Unknown cases at the first stage, and contribute to the improvement of the overall performance in the RTE task. In addition, several heterogeneous lexical resources are tested, and different contri- butions from them are observed.