Information extraction systems often produce hundreds to thousands of strings
on a specific topic. We present a method that facilitates better consumption of
these strings, in an exploratory setting in which a user wants to both get a
broad overview of what's available, and a chance to dive deeper on some
aspects. The system works by grouping similar items together and arranging the
remaining items into a hierarchical navigable DAG structure. We apply the
method to medical information extraction.

该研究介绍了一种方法，通过将相似的项目分组并将其余项目排列为分层可导航的有向无环图结构，从而在医疗信息提取方面实现更好的消耗。

层级构建器：将文本片段组织为层级以便于导航

Hierarchy Builder: Organizing Textual Spans into a Hierarchy to  Facilitate Navigation

We present an empirical study on methods for span finding, the selection of
consecutive tokens in text for some downstream tasks. We focus on approaches
that can be employed in training end-to-end information extraction systems, and
find there is no definitive solution without considering task properties, and
provide our observations to help with future design choices: 1) a tagging
approach often yields higher precision while span enumeration and boundary
prediction provide higher recall; 2) span type information can benefit a
boundary prediction approach; 3) additional contextualization does not help
span finding in most cases.

本文是关于跨度查找的实证研究，着重探讨了可用于训练端到端信息提取系统的方法，并发现在不考虑任务属性的情况下，没有明确的解决方案。观察发现：1）标记方法通常具有更高的精度，而跨度枚举和边界预测提供更高的召回率；2）跨度类型信息对边界预测方法有益；3）在大多数情况下，额外的上下文信息对跨度查找没有帮助。

寻找跨度的实证研究

An Empirical Study on Finding Spans

Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, extraction from new sources and reconciliation of extracted values,
which are repeated until sufficient evidence is collected. We approach the
problem using a reinforcement learning framework where our model learns to
select optimal actions based on contextual information. We employ a deep
Q-network, trained to optimize a reward function that reflects extraction
accuracy while penalizing extra effort. Our experiments on two databases -- of
shooting incidents, and food adulteration cases -- demonstrate that our system
significantly outperforms traditional extractors and a competitive
meta-classifier baseline.

本文研究了在训练数据稀缺的情况下，通过获取和整合外部证据来提高信息提取系统的准确性的任务，并利用增强学习框架实现优化操作以提高精度的深度 Q 网络，在枪击事件和食品欺诈案件等两个数据库中实现了显著的性能提升。