Existing event-centric NLP models often only apply to the pre-defined
ontology, which significantly restricts their generalization capabilities. This
paper presents CEO, a novel Corpus-based Event Ontology induction model to
relax the restriction imposed by pre-defined event ontologies. Without direct
supervision, CEO leverages distant supervision from available summary datasets
to detect corpus-wise salient events and exploits external event knowledge to
force events within a short distance to have close embeddings. Experiments on
three popular event datasets show that the schema induced by CEO has better
coverage and higher accuracy than previous methods. Moreover, CEO is the first
event ontology induction model that can induce a hierarchical event ontology
with meaningful names on eleven open-domain corpora, making the induced schema
more trustworthy and easier to be further curated.

本文提出一种新型的 CEO 语料库事件本体感知模型，可以通过利用语料库中的显著性事件和外部事件知识，来缓解预定义事件本体所带来的数量限制问题，并且可以诱导具有意义的层次结构事件本体，在 11 种领域中有更好的覆盖率和更高的准确性。

基于语料库的开放域事件本体诱导

CEO: Corpus-based Open-Domain Event Ontology Induction

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。

基于语料库的开放领域事件类型归纳

Corpus-based Open-Domain Event Type Induction

Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based
set expansion is to induce an extensive set of entities which share the same
semantic class (Country in this example) from a given corpus. Set expansion
benefits a wide range of downstream applications in knowledge discovery, such
as web search, taxonomy construction, and query suggestion. Existing
corpus-based set expansion algorithms typically bootstrap the given seeds by
incorporating lexical patterns and distributional similarity. However, due to
no negative sets provided explicitly, these methods suffer from semantic drift
caused by expanding the seed set freely without guidance. We propose a new
framework, Set-CoExpan, that automatically generates auxiliary sets as negative
sets that are closely related to the target set of user's interest, and then
performs multiple sets co-expansion that extracts discriminative features by
comparing target set with auxiliary sets, to form multiple cohesive sets that
are distinctive from one another, thus resolving the semantic drift issue. In
this paper we demonstrate that by generating auxiliary sets, we can guide the
expansion process of target set to avoid touching those ambiguous areas around
the border with auxiliary sets, and we show that Set-CoExpan outperforms strong
baseline methods significantly.

本文提出了一种新的框架 Set-CoExpan，它可以自动生成辅助负集作为联系紧密的与用户所需目标集相关的负向集，通过将目标集与辅助集进行比较提取区分特征，形成多个互不相同的相互关联的集合，从而解决语义漂移问题。实验证明，通过生成辅助集，可以引导目标集的扩展过程，从而避免触及边界周围的模糊区域，同时 Set-CoExpan 可以显著优于强基线方法。