@inproceedings{li-etal-2017-initializing,
    title = "Initializing Convolutional Filters with Semantic Features for Text Classification",
    author = "Li, Shen  and
      Zhao, Zhe  and
      Liu, Tao  and
      Hu, Renfen  and
      Du, Xiaoyong",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D17-1201/",
    doi = "10.18653/v1/D17-1201",
    pages = "1884--1889",
    abstract = "Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification."
}