Optimizing Textual Entailment Recognition Using Particle Swarm Optimization
Yashar Mehdad and Bernardo Magnini
In: ACL 2009 Workshop on Applied Textual Inference (TextInfer), August 6, 2009, Singapore.
This paper introduces a new method to im- prove tree edit distance approach to tex- tual entailment recognition, using particle swarm optimization. Currently, one of the main constraints of recognizing textual en- tailment using tree edit distance is to tune the cost of edit operations, which is a dif- ficult and challenging task in dealing with the entailment problem and datasets. We tried to estimate the cost of edit operations in tree edit distance algorithm automati- cally, in order to improve the results for textual entailment. Automatically estimat- ing the optimal values of the cost opera- tions over all RTE development datasets, we proved a significant enhancement in accuracy obtained on the test sets.