Paper: Tree Edit Models for Recognizing Textual Entailments Paraphrases and Answers to Questions

ACL ID N10-1145
Title Tree Edit Models for Recognizing Textual Entailments Paraphrases and Answers to Questions
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demon- strate that they offer a simple, intuitive, and effective method for modeling pairs of seman- tically 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 clas- sify the sentence pairs. The approach leads to competitive performance in recognizing tex- tual entailment, paraphrase identification, and answer selection for question answering.