@inproceedings{lee-etal-2017-nctu,
    title = "{NCTU}-{NTUT} at {IJCNLP}-2017 Task 2: Deep Phrase Embedding using bi-{LSTM}s for Valence-Arousal Ratings Prediction of {C}hinese Phrases",
    author = "Lee, Yen-Hsuan  and
      Yeh, Han-Yun  and
      Wang, Yih-Ru  and
      Liao, Yuan-Fu",
    editor = "Liu, Chao-Hong  and
      Nakov, Preslav  and
      Xue, Nianwen",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/fix-sig-urls/I17-4020/",
    pages = "124--129",
    abstract = "In this paper, a deep phrase embedding approach using bi-directional long short-term memory (Bi-LSTM) is proposed to predict the valence-arousal ratings of Chinese words and phrases. It adopts a Chinese word segmentation frontend, a local order-aware word, a global phrase embedding representations and a deep regression neural network (DRNN) model. The performance of the proposed method was benchmarked by the IJCNLP 2017 shared task 2. According the official evaluation results, our best system achieved mean rank 6.5 among all 24 submissions."
}