In medical documents, it is possible that an entity of interest not only
contains a discontiguous sequence of words but also overlaps with another
entity. Entities of such structures are intrinsically hard to recognize due to
the large space of possible entity combinations. In this work, we propose a
neural two-stage approach to recognize discontiguous and overlapping entities
by decomposing this problem into two subtasks: 1) it first detects all the
overlapping spans that either form entities on their own or present as segments
of discontiguous entities, based on the representation of segmental hypergraph,
2) next it learns to combine these segments into discontiguous entities with a
classifier, which filters out other incorrect combinations of segments. Two
neural components are designed for these subtasks respectively and they are
learned jointly using a shared encoder for text. Our model achieves the
state-of-the-art performance in a standard dataset, even in the absence of
external features that previous methods used.

本文提出了一种神经网络模型来识别医学文档中具有不连续序列和重叠结构的实体，通过将这个问题分解成两个子任务：检测所有重叠跨度，并将这些跨度组合成不连续的实体。使用两个神经组件学习这些子任务的过程，并能够在标准数据集上实现最先进的性能。

将跨度合并为实体：识别不连续实体的神经两阶段方法

Combining Spans into Entities: A Neural Two-Stage Approach for  Recognizing Discontiguous Entities

This paper focuses on the study of recognizing discontiguous entities.
Motivated by a previous work, we propose to use a novel hypergraph
representation to jointly encode discontiguous entities of unbounded length,
which can overlap with one another. To compare with existing approaches, we
first formally introduce the notion of model ambiguity, which defines the
difficulty level of interpreting the outputs of a model, and then formally
analyze the theoretical advantages of our model over previous existing
approaches based on linear-chain CRFs. Our empirical results also show that our
model is able to achieve significantly better results when evaluated on
standard data with many discontiguous entities.

该研究聚焦于识别不连续实体，通过使用新的超图表示方法来编码不连续且重叠的实体，形成一个独特的模型。研究结果表明，该模型在处理大量不连续实体的数据时显示出较强的优势。