@inproceedings{sagot-martinez-alonso-2017-improving,
    title = "Improving neural tagging with lexical information",
    author = "Sagot, Beno{\^i}t  and
      Mart{\'i}nez Alonso, H{\'e}ctor",
    editor = "Miyao, Yusuke  and
      Sagae, Kenji",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W17-6304/",
    pages = "25--31",
    abstract = "Neural part-of-speech tagging has achieved competitive results with the incorporation of character-based and pre-trained word embeddings. In this paper, we show that a state-of-the-art bi-LSTM tagger can benefit from using information from morphosyntactic lexicons as additional input. The tagger, trained on several dozen languages, shows a consistent, average improvement when using lexical information, even when also using character-based embeddings, thus showing the complementarity of the different sources of lexical information. The improvements are particularly important for the smaller datasets."
}