Paper: Answer Extraction as Sequence Tagging with Tree Edit Distance

ACL ID N13-1106
Title Answer Extraction as Sequence Tagging with Tree Edit Distance
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Our goal is to extract answers from pre- retrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer types. This casts answer extraction as an an- swer sequence tagging problem for the first time, where knowledge of shared structure be- tween question and source sentence is incor- porated through features based on Tree Edit Distance (TED). Our model is free of man- ually created question and answer templates, fast to run (processing 200 QA pairs per sec- ond excluding parsing time), and yields an F1 of 63.3% on a new public dataset based on prior TREC QA evaluations. The developed system is open-source, and includes an imple- menta...