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.
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