A Stepwise Detection of Conjunctive Structures in Questions using Maximum Entropy Model
Yaoyun Zhang, Xuan Wang, Xiaolong Wang, Shixi Fan · 2007
This paper presents a maximum entropy model approach to identifying conjuncts of conjunctive structures in questions of financial domain from on-line discussion groups. To avoid phrasal ambiguity, only features in lexical and shallow syntactic level are used. The conjunct detection problem is converted into a stepwise boundary identification task, reducing the search space of a n-word sentence from O(n2) to O(n), The best performance on the test set achieves 85.88% recall and 96% rejection. This approach itself is domain-independent and can be used for conjunct identification in questions universally.