Archive for June 29th, 2007

Machine Learning Based Semantic Inference: Experiments and Observations at RTE-3

Textual Entailment Recognition is a semantic inference task that is required in many natural language processing (NLP) applications. In this paper, we present our system for the third PASCAL recognizing textual entailment (RTE-3) challenge. The system is built on a machine learning framework with the following features derived by state-of-the-art NLP techniques: lexical semantic similarity (LSS), named entities (NE), dependent content word pairs (DEP), average distance (DIST), negation (NG), task (TK), and text length (LEN).

On the RTE-3 test dataset, our system achieves the accuracy of 0.64 and 0.6488 for the two official submissions, respectively. Experimental results show that LSS and NE are the most effective features. Further analyses indicate that a baseline dummy system can achieve accuracy 0.545 on the RTE-3 test dataset, which makes RTE-3 relatively easier than RTE-2 and RTE-1. In addition, we demonstrate with examples that the current Average Precision measure and its evaluation process need to be changed.

Read the paper:

Machine Learning Based Semantic Inference: Experiments and Observations at RTE-3

by Baoli Li, Joseph Irwin, Ernie Garcia, Ashwin Ram

Association for Computational Linguistics (ACL) Challenge Workshop on Textual Entailment and Paraphrase (WTEP-07), Prague, Czech Republic, June 2007