@inproceedings{vacareanu-etal-2022-patternrank,
    title = "{P}attern{R}ank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms",
    author = "Vacareanu, Robert  and
      Bell, Dane  and
      Surdeanu, Mihai",
    editor = "Chiticariu, Laura  and
      Goldberg, Yoav  and
      Hahn-Powell, Gus  and
      Morrison, Clayton T.  and
      Naik, Aakanksha  and
      Sharp, Rebecca  and
      Surdeanu, Mihai  and
      Valenzuela-Esc{\'a}rcega, Marco  and
      Noriega-Atala, Enrique",
    booktitle = "Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Conference on Computational Linguistics",
    url = "https://aclanthology.org/2022.pandl-1.1",
    pages = "1--10",
    abstract = "In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today{'}s ubiquitous neural classifiers. We propose a semi-supervised graph-based algorithm for pattern acquisition that scores patterns and the relations they extract jointly, using a variant of PageRank. We insert light supervision in the form of seed patterns or relations, and model it with several custom teleportation probabilities that bias random-walk scores of patterns/relations based on their proximity to correct information. We evaluate our approach on Few-Shot TACRED, and show that our method outperforms (or performs competitively with) more expensive and opaque deep neural networks. Lastly, we thoroughly compare our proposed approach with the seminal RlogF pattern acquisition algorithm of, showing that it outperforms it for all the hyper parameters tested, in all settings.",
}