Answer Extraction as Sequence Tagging with Tree Edit Distance
Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch, Peter E. Clark · 2013
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-mentation of the TED model that is state of the art in the task of ranking QA pairs. 1