@inproceedings{park-etal-2018-knu,
    title = "{KNU} {CI} System at {S}em{E}val-2018 Task4: Character Identification by Solving Sequence-Labeling Problem",
    author = "Park, Cheoneum  and
      Song, Heejun  and
      Lee, Changki",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S18-1107/",
    doi = "10.18653/v1/S18-1107",
    pages = "655--659",
    abstract = "Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder{--}decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder{--}decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder{--}decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00{\%} and a label accuracy of 85.10{\%} at the scene-level."
}