HowNet Based Chinese Question Automatic Classification
Dong Yan-ju · Zhongwen xinxi xuebao · 2007
Question answering system can provides a precise and concise answer to a natural language query.Question classification is the first task of Question Answering System,and the precision of question classification has great effect on the subsequent processes.In this paper,we present a new method on feature extraction which uses HowNet as semantic resource,and use Maximum Entropy Model to realize it.We choose the interrogative words,syntax structure,question focus words and their first sememes as classification feature.The experiment result show that the first sememes in HowNet can express the main meaning of the question focus words,it can be as an important feature.This method can improve the precision of question classification: the classification precision of coarse classes and fine classes reaches 92.18% and 83.86% respectively.