We propose a graph-based event extraction framework JSEEGraph that approaches
the task of event extraction as general graph parsing in the tradition of
Meaning Representation Parsing. It explicitly encodes entities and events in a
single semantic graph, and further has the flexibility to encode a wider range
of additional IE relations and jointly infer individual tasks. JSEEGraph
performs in an end-to-end manner via general graph parsing: (1) instead of flat
sequence labelling, nested structures between entities/triggers are efficiently
encoded as separate nodes in the graph, allowing for nested and overlapping
entities and triggers; (2) both entities, relations, and events can be encoded
in the same graph, where entities and event triggers are represented as nodes
and entity relations and event arguments are constructed via edges; (3) joint
inference avoids error propagation and enhances the interpolation of different
IE tasks. We experiment on two benchmark datasets of varying structural
complexities; ACE05 and Rich ERE, covering three languages: English, Chinese,
and Spanish. Experimental results show that JSEEGraph can handle nested event
structures, that it is beneficial to solve different IE tasks jointly, and that
event argument extraction in particular benefits from entity extraction. Our
code and models are released as open-source.

JSEEGraph 是一个基于图的事件抽取框架，采用 “意义表示解析” 的方式来处理事件抽取任务。该模型能够同时编码实体和事件，并能够进行其他信息抽取关系的编码和联合推理，并通过构图的方式处理实体和事件的嵌套结构。实验结果表明，JSEEGraph 具有很好的性能，尤其是可以处理嵌套事件结构和联合解决不同的信息抽取任务，代码和模型已经得到公开发布。

JSEEGraph: 联合结构化事件抽取作为图解析

JSEEGraph: Joint Structured Event Extraction as Graph Parsing

We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced
Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline
consisting of off-the-shelf models for everything but enhanced graph parsing,
and for the latter, a transition-based graph parser adapted from Che et al.
(2019). We train a single enhanced parser model per language, using gold
sentence splitting and tokenization for training, and rely only on tokenized
surface forms and multilingual BERT for encoding. While a bug introduced just
before submission resulted in a severe drop in precision, its post-submission
fix would bring us to 4th place in the official ranking, according to average
ELAS. Our parser demonstrates that a unified pipeline is effective for both
Meaning Representation Parsing and Enhanced Universal Dependencies.

我们提出了 Køpsala 系统，它通过一个统一管道实现了增强型通用依赖关系和意义表示的解析，并利用适应自 Che 等人（2019）的基于转换的图形解析器来完成增强型图形解析。