@inproceedings{mu-etal-2019-learning,
    title = "Learning Outside the Box: Discourse-level Features Improve Metaphor Identification",
    author = "Mu, Jesse  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N19-1059/",
    doi = "10.18653/v1/N19-1059",
    pages = "596--601",
    abstract = "Most current approaches to metaphor identification use restricted linguistic contexts, e.g. by considering only a verb`s arguments or the sentence containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for better metaphor identification. We train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without the complex metaphor-specific features or deep neural architectures employed by other systems. A qualitative analysis further confirms the need for broader context in metaphor processing."
}