@inproceedings{chen-etal-2019-bag,
    title = "A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data",
    author = "Chen, Jiyu  and
      Verspoor, Karin  and
      Zhai, Zenan",
    editor = "Kar, Sudipta  and
      Nadeem, Farah  and
      Burdick, Laura  and
      Durrett, Greg  and
      Han, Na-Rae",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/N19-3007/",
    doi = "10.18653/v1/N19-3007",
    pages = "43--52",
    abstract = "This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset."
}