Word Substitution in Short Answer Extraction: A WordNet-based Approach
Qingqing Cai, James Gung, Maochen Guan, Gerald Kurlandski, Adam Pease · 2016
We describe the implementation of a short answer extraction system.It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence.In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UIUC dataset and taxonomy and a feature set including word vectors.This phase outperforms the current best published results on that dataset.In the second phase, a sieve algorithm consisting of a series of increasingly general extraction rules is applied, using WordNet to find word types aligned with the UIUC classifications determined in the first phase.Some very preliminary performance metrics are presented.