@inProceedings{
 title = {Exploring correlation of dependency relation paths for answer extraction},
 type = {inProceedings},
 year = {2006},
 identifiers = {[object Object]},
 pages = {889-896},
 issue = {July},
 websites = {http://portal.acm.org/citation.cfm?doid=1220175.1220287},
 publisher = {Association for Computational Linguistics},
 id = {d67ddf2b-f2a3-3721-bf8d-0b4299b1c2a9},
 created = {2010-11-06T02:48:29.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Shen2006},
 private_publication = {false},
 abstract = {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.},
 bibtype = {inProceedings},
 author = {Shen, Dan and Klakow, Dietrich},
 booktitle = {21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics}
}

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