Paper: Exploring Correlation Of Dependency Relation Paths For Answer Extraction

ACL ID P06-1112
Title Exploring Correlation Of Dependency Relation Paths For Answer Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

In this paper, we explore correlation of dependency relation paths to rank candi- date answers in answer extraction. Using the correlation measure, we compare de- pendency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in ques- tion. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from train- ing. Experimental results show that our method significantly outperforms state-of- the-art syntactic relation-based methods by up to 20% in MRR.