@inproceedings{che-zhang-2017-deep,
    title = "Deep Learning in Lexical Analysis and Parsing",
    author = "Che, Wanxiang  and
      Zhang, Yue",
    editor = "Kurohashi, Sadao  and
      Strube, Michael",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://aclanthology.org/I17-5001",
    pages = "1--2",
    abstract = "Neural networks, also with a fancy name deep learning, just right can overcome the above {``}feature engineering{''} problem. In theory, they can use non-linear activation functions and multiple layers to automatically find useful features. The novel network structures, such as convolutional or recurrent, help to reduce the difficulty further. These deep learning models have been successfully used for lexical analysis and parsing. In this tutorial, we will give a review of each line of work, by contrasting them with traditional statistical methods, and organizing them in consistent orders.",
}