@inproceedings{feng-etal-2017-beihang,
    title = "Beihang-{MSRA} at {S}em{E}val-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering",
    author = "Feng, Wenzheng  and
      Wu, Yu  and
      Wu, Wei  and
      Li, Zhoujun  and
      Zhou, Ming",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/S17-2045/",
    doi = "10.18653/v1/S17-2045",
    pages = "280--286",
    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."
}