Named Entity Recognition (NER) is a fundamental task in Natural Language
Processing, concerned with identifying spans of text expressing references to
entities. NER research is often focused on flat entities only (flat NER),
ignoring the fact that entity references can be nested, as in [Bank of [China]]
(Finkel and Manning, 2009). In this paper, we use ideas from graph-based
dependency parsing to provide our model a global view on the input via a
biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of
start and end tokens in a sentence which we use to explore all spans, so that
the model is able to predict named entities accurately. We show that the model
works well for both nested and flat NER through evaluation on 8 corpora and
achieving SoTA performance on all of them, with accuracy gains of up to 2.2
percentage points.

本文基于图形依存解析思想，利用双仿射模型对输入进行全局视图，探索所有语句中的跨度，以准确预测命名实体，以 Nested NER 为例， 对 8 个语料库进行了评估并取得了所有语料库的 SOTA 性能，最高的准确度增益高达 2.2 个百分点。

实体识别作为依存句法分析

Named Entity Recognition as Dependency Parsing

The run time complexity of state-of-the-art inference algorithms in
graph-based dependency parsing is super-linear in the number of input words
(n). Recently, pruning algorithms for these models have shown to cut a large
portion of the graph edges, with minimal damage to the resulting parse trees.
Solving the inference problem in run time complexity determined solely by the
number of edges (m) is hence of obvious importance.
We propose such an inference algorithm for first-order models, which encodes
the problem as a minimum spanning tree (MST) problem in an undirected graph.
This allows us to utilize state-of-the-art undirected MST algorithms whose run
time is O(m) at expectation and with a very high probability. A directed parse
tree is then inferred from the undirected MST and is subsequently improved with
respect to the directed parsing model through local greedy updates, both steps
running in O(n) time. In experiments with 18 languages, a variant of the
first-order MSTParser (McDonald et al., 2005b) that employs our algorithm
performs very similarly to the original parser that runs an O(n^2) directed MST
inference.

该研究提出了一种基于最小生成树的推断算法，可以将大规模的图形依赖分析模型边缘数量的运行时间复杂度降至 O (m)，并通过局部贪心更新进一步优化了有向解析树模型，验证实验表明，该算法在 18 种语言的测试中表现类似于原始解析器。