@inproceedings{liang-etal-2017-combining,
    title = "Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text",
    author = "Liang, Dongyun  and
      Xu, Weiran  and
      Zhao, Yinge",
    editor = "Blunsom, Phil  and
      Bordes, Antoine  and
      Cho, Kyunghyun  and
      Cohen, Shay  and
      Dyer, Chris  and
      Grefenstette, Edward  and
      Hermann, Karl Moritz  and
      Rimell, Laura  and
      Weston, Jason  and
      Yih, Scott",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-2606/",
    doi = "10.18653/v1/W17-2606",
    pages = "43--47",
    abstract = "Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words may carry important short-distance semantic information. We propose a hybrid model, combining the merits of word-level and character-level representations to learn better representations on informal text. Experiments on two dataset of relation classification, SemEval-2010 Task8 and a large-scale one we compile from informal text, show that our model achieves a competitive result in the former and state-of-the-art with the other."
}