Events are essential components of speech and texts, describing the changes
in the state of entities. The event extraction task aims to identify and
classify events and find their participants according to event schemas.
Manually predefined event schemas have limited coverage and are hard to migrate
across domains. Therefore, the researchers propose Liberal Event Extraction
(LEE), which aims to extract events and discover event schemas simultaneously.
However, existing LEE models rely heavily on external language knowledge bases
and require the manual development of numerous rules for noise removal and
knowledge alignment, which is complex and laborious. To this end, we propose a
Prompt-based Graph Model for Liberal Event Extraction (PGLEE). Specifically, we
use a prompt-based model to obtain candidate triggers and arguments, and then
build heterogeneous event graphs to encode the structures within and between
events. Experimental results prove that our approach achieves excellent
performance with or without predefined event schemas, while the automatically
detected event schemas are proven high quality.

事件抽取任务旨在识别和分类事件，并根据事件模式找到其参与者。我们提出了一种基于提示的图模型用于自由事件抽取，该模型使用基于提示的方法获取候选触发器和参数，并构建异构事件图来编码事件内部和事件间的结构。实验结果表明，我们的方法在预定义事件模式下或无预定义事件模式下都能取得出色的性能，并且自动检测到的事件模式质量高。

基于提示的图模型用于联合自由事件抽取和事件模式归纳

Prompt-based Graph Model for Joint Liberal Event Extraction and Event  Schema Induction

Event schemas are a form of world knowledge about the typical progression of
events. Recent methods for event schema induction use information extraction
systems to construct a large number of event graph instances from documents,
and then learn to generalize the schema from such instances. In contrast, we
propose to treat event schemas as a form of commonsense knowledge that can be
derived from large language models (LLMs). This new paradigm greatly simplifies
the schema induction process and allows us to handle both hierarchical
relations and temporal relations between events in a straightforward way. Since
event schemas have complex graph structures, we design an incremental prompting
and verification method to break down the construction of a complex event graph
into three stages: event skeleton construction, event expansion, and
event-event relation verification. Compared to directly using LLMs to generate
a linearized graph, our method can generate large and complex schemas with 7.2%
F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical
relations. In addition, compared to the previous state-of-the-art closed-domain
schema induction model, human assessors were able to cover $\sim$10% more
events when translating the schemas into coherent stories and rated our schemas
1.3 points higher (on a 5-point scale) in terms of readability.

通过利用大型语言模型，将事件图谱识别为一种常识性知识形式，并提出一种增量提示和验证方法，可以直接处理事件之间的层次关系和时间关系。相较于其他方法，可以提高 F1 值。

增量提示和验证的开放域层次事件模式归纳

Open-Domain Hierarchical Event Schema Induction by Incremental Prompting  and Verification

News agencies produce thousands of multimedia stories describing events
happening in the world that are either scheduled such as sports competitions,
political summits and elections, or breaking events such as military conflicts,
terrorist attacks, natural disasters, etc. When writing up those stories,
journalists refer to contextual background and to compare with past similar
events. However, searching for precise facts described in stories is hard. In
this paper, we propose a general method that leverages the Wikidata knowledge
base to produce semantic annotations of news articles. Next, we describe a
semantic search engine that supports both keyword based search in news articles
and structured data search providing filters for properties belonging to
specific event schemas that are automatically inferred.

本文提出一种使用 Wikidata 知识库的方法来产生新闻文章的语义注释，并描述了一种语义搜索引擎，支持基于关键词的搜索和结构化数据搜索。