Extracting Answers To Natural Language Questions From Large-Scale Corpus
Peng Li, Xiao-Long Wang, Yi Guan, Yuming Zhao · 2006
This paper provides a novel and tractable method for extracting exact textual answers from the returned documents that are retrieved by traditional IR system in large-scale collection of texts. In our approach, WordNet and Web information are employed to improve the performance as external auxiliary resources, then some NLP technologies are used to constitute the empirical answer ranking formula, such as POS tagging, Named Entity recognition, and parser etc. The method involves automatically ranking passages with System Similarity Model, automatically downloading related Web pages by means of Web crawler, and automatically mining answers with empirical formula from candidate answer sets. The series of experimental results show that the overall performance of our system is good and the structure of the system is reasonable.