Recognizing Textual Relatedness with Predicate-Argument Structures

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 measurement 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 contributions from them are observed.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Rui Wang 0005, Yi Zhang 0003
• Keywords: EMNLP 2009, Heterogeneous Lexical Resources, Natural Language Processing, RTE Task, Textual Entailment