We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demonstrate that they offer a simple, intuitive, and effective method for modeling pairs of semantically related sentences. To efficiently extract sequences of edits, we employ a tree kernel as a heuristic in a greedy search routine. We describe a logistic regression model that uses 33 syntactic features of edit sequences to classify the sentence pairs. The approach leads to competitive performance in recognizing textual entailment, paraphrase identification, and answer selection for question answering.
REF applied Tree Edit Distance (TED) for learning tree transformations in a Q/AP pair.
Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science