Event extraction (EE) is an essential task of information extraction, which
aims to extract structured event information from unstructured text. Most prior
work focuses on extracting flat events while neglecting overlapped or nested
ones. A few models for overlapped and nested EE includes several successive
stages to extract event triggers and arguments,which suffer from error
propagation. Therefore, we design a simple yet effective tagging scheme and
model to formulate EE as word-word relation recognition, called OneEE. The
relations between trigger or argument words are simultaneously recognized in
one stage with parallel grid tagging, thus yielding a very fast event
extraction speed. The model is equipped with an adaptive event fusion module to
generate event-aware representations and a distance-aware predictor to
integrate relative distance information for word-word relation recognition,
which are empirically demonstrated to be effective mechanisms. Experiments on 3
overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show
that OneEE achieves the state-of-the-art (SOTA) results. Moreover, the
inference speed of OneEE is faster than those of baselines in the same
condition, and can be further substantially improved since it supports parallel
inference.

OneEE 是一种简单而有效的标注方案和模型，将事件提取作为词词关系识别来表述，它在同时识别触发词或参数词之间的关系方面表现出色，具有很快的事件提取速度，并在 FewFC、Genia11 和 Genia13 三个基准测试上取得了最先进的结果。

OneEE：一种快速重叠和嵌套事件提取的一阶段框架

OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction

Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and their relations directly, without identifying entities and
relations separately. We conduct experiments on a public dataset produced by
distant supervision method and the experimental results show that the tagging
based methods are better than most of the existing pipelined and joint learning
methods. What's more, the end-to-end model proposed in this paper, achieves the
best results on the public dataset.

该研究提出了一种基于标记方案的端到端关系抽取模型，将实体和关系一起提取转化成一个标记问题并在公共数据集上实现了最佳结果。