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 <predicate sense, object head> 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 <predicate sense, object head> 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.

本文提出了一种基于语料库的开放领域事件类型归纳方法，该方法使用聚类 <谓词意义，对象头> 对每种事件类型进行表示，并利用选择显著谓词和对象头、利用动词意义词典消歧谓词语义、联合嵌入和聚类 <谓词意义，对象头> 对事件类型进行潜在球面空间的建模。多项实验表明，该方法能够有效地发现具有重要意义和高质量的事件类型。