In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. 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 training. Experimental results show that our method significantly outperforms state-ofthe-art syntactic relation-based methods by up to 20% in MRR.
Secondly, REF use dependency relation paths to rank answer candidates.
Exploring Correlation Of Dependency Relation Paths For Answer Extraction
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics
Computer Science