Automatic cost estimation for tree edit distance using particle swarm optimization
Recently, there is a growing interest in working with tree-structured data in different applications and domains such as computational biology and natural language processing. Moreover, many applications in computational linguistics require the computation of similarities over pair of syntactic or semantic trees. In this context, Tree Edit Distance (TED) has been widely used for many years. However, one of the main constraints of this method is to tune the cost of edit operations, which makes it difficult or sometimes very challenging in dealing with complex problems. In this paper, we propose an original method to estimate and optimize the operation costs in TED, applying the Particle Swarm Optimization algorithm. Our experiments on Recognizing Textual Entailment show the success of this method in automatic estimation, rather than manual assignment of edit costs.