EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering

Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, Zhao Lu

Abstract
We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50%.
Anthology ID:
S17-2047
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
292–298
Language:
URL:
https://aclanthology.org/S17-2047
DOI:
10.18653/v1/S17-2047
Bibkey:
Cite (ACL):
Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, and Zhao Lu. 2017. EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 292–298, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering (Xie et al., SemEval 2017)
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