@inproceedings{shen-etal-2021-corpus,
    title = "Corpus-based Open-Domain Event Type Induction",
    author = "Shen, Jiaming  and
      Zhang, Yunyi  and
      Ji, Heng  and
      Han, Jiawei",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.emnlp-main.441/",
    doi = "10.18653/v1/2021.emnlp-main.441",
    pages = "5427--5440",
    abstract = "Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of {\ensuremath{<}}predicate sense, object head{\ensuremath{>}} pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering {\ensuremath{<}}predicate sense, object head{\ensuremath{>}} pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations."
}