Event linking connects event mentions in text with relevant nodes in a
knowledge base (KB). Prior research in event linking has mainly borrowed
methods from entity linking, overlooking the distinct features of events.
Compared to the extensively explored entity linking task, events have more
complex structures and can be more effectively distinguished by examining their
associated arguments. Moreover, the information-rich nature of events leads to
the scarcity of event KBs. This emphasizes the need for event linking models to
identify and classify event mentions not in the KB as ``out-of-KB,'' an area
that has received limited attention. In this work, we tackle these challenges
by introducing an argument-aware approach. First, we improve event linking
models by augmenting input text with tagged event argument information,
facilitating the recognition of key information about event mentions.
Subsequently, to help the model handle ``out-of-KB'' scenarios, we synthesize
out-of-KB training examples from in-KB instances through controlled
manipulation of event arguments. Our experiment across two test datasets showed
significant enhancements in both in-KB and out-of-KB scenarios, with a notable
22% improvement in out-of-KB evaluations.

事件链接通过识别事件提及与知识库中相关的节点相连。该研究提出了一种关注事件参数的方法，以解决事件链接中的若干挑战，包括事件参数信息的识别和处理知识库外事件提及的问题。在两个测试数据集上进行的实验证明，该方法在处理知识库内外的情况下都取得了显著的改进，其中处理知识库外事件提及的效果提高了 22%。

事件链接的论证感知方法

Argument-Aware Approach To Event Linking

Comprehending an article requires understanding its constituent events.
However, the context where an event is mentioned often lacks the details of
this event. A question arises: how can the reader obtain more knowledge about
this particular event in addition to what is provided by the local context in
the article?
This work defines Event Linking, a new natural language understanding task at
the event level. Event linking tries to link an event mention appearing in an
article to the most appropriate Wikipedia page. This page is expected to
provide rich knowledge about what the event mention refers to. To standardize
the research in this new direction, we contribute in four-fold. First, this is
the first work in the community that formally defines Event Linking task.
Second, we collect a dataset for this new task. Specifically, we automatically
gather training set from Wikipedia, and then create two evaluation sets: one
from the Wikipedia domain, reporting the in-domain performance, and a second
from the real-world news domain, to evaluate out-of-domain performance. Third,
we retrain and evaluate two state-of-the-art (SOTA) entity linking models,
showing the challenges of event linking, and we propose an event-specific
linking system EVELINK to set a competitive result for the new task. Fourth, we
conduct a detailed and insightful analysis to help understand the task and the
limitation of the current model. Overall, as our analysis shows, Event Linking
is a considerably challenging and essential task requiring more effort from the
community. Data and code are available here:
this https URL

本研究定义了事件关联，这是一种新的自然语言理解任务，尝试将文章中出现的事件与最相关的维基百科页面链接起来，并贡献了一份数据集用于此任务，同时对两个最新的实体链接模型进行重新训练和评估，并提出了一种用于此任务的竞争性系统 EVELINK。