Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering

Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou


Abstract
This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA). We develop a ranking system that is capable of capturing semantic relations between text pairs with little word overlap. In addition to traditional NLP features, we introduce several neural network based matching features which enable our system to measure text similarity beyond lexicons. Our system significantly outperforms baseline methods and holds the second place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy on answer selection and question retrieval.
Anthology ID:
S17-2045
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
280–286
Language:
URL:
https://www.aclweb.org/anthology/S17-2045
DOI:
10.18653/v1/S17-2045
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2045.pdf