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
- 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:
- 280–286
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/S17-2045/
- DOI:
- 10.18653/v1/S17-2045
- Cite (ACL):
- Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, and Ming Zhou. 2017. Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 280–286, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering (Feng et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/S17-2045.pdf