This paper introduces a new method to improve tree edit distance approach to textual entailment recognition, using particle swarm optimization. Currently, one of the main constraints of recognizing textual entailment using tree edit distance is to tune the cost of edit operations, which is a difficult 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 automatically, in order to improve the results for textual entailment. Automatically estimating the optimal values of the cost operations over all RTE development datasets, we proved a significant enhancement in accuracy obtained on the test sets.
Optimizing Textual Entailment Recognition Using Particle Swarm Optimization
Mehdad, Yashar;Magnini, Bernardo
2009-01-01
Abstract
This paper introduces a new method to improve tree edit distance approach to textual entailment recognition, using particle swarm optimization. Currently, one of the main constraints of recognizing textual entailment using tree edit distance is to tune the cost of edit operations, which is a difficult 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 automatically, in order to improve the results for textual entailment. Automatically estimating the optimal values of the cost operations over all RTE development datasets, we proved a significant enhancement in accuracy obtained on the test sets.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
