@inproceedings{jochim-deleris-2017-named,
    title = "Named Entity Recognition in the Medical Domain with Constrained {CRF} Models",
    author = "Jochim, Charles  and
      Deleris, L{\'e}a",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/E17-1079/",
    pages = "839--849",
    abstract = "This paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence relations and probability statements. We explore whether adding constraints can lead to an improvement over standard CRF decoding. Results on our relation extraction tasks are promising, showing significant increases in performance from both (i) adding constraints to post-process the output of a baseline CRF, which captures {\textquotedblleft}domain knowledge{\textquotedblright}, and (ii) further allowing flexibility in the application of those constraints by leveraging a binary classifier as a pre-processing step."
}