Clinical notes containing valuable patient information are written by
different health care providers with various scientific levels and writing
styles. It might be helpful for clinicians and researchers to understand what
information is essential when dealing with extensive electronic medical
records. Entities recognizing and mapping them to standard terminologies is
crucial in reducing ambiguity in processing clinical notes. Although named
entity recognition and entity linking are critical steps in clinical natural
language processing, they can also result in the production of repetitive and
low-value concepts. In other hand, all parts of a clinical text do not share
the same importance or content in predicting the patient's condition. As a
result, it is necessary to identify the section in which each content is
recorded and also to identify key concepts to extract meaning from clinical
texts. In this study, these challenges have been addressed by using clinical
natural language processing techniques. In addition, in order to identify key
concepts, a set of popular unsupervised key phrase extraction methods has been
verified and evaluated. Considering that most of the clinical concepts are in
the form of multi-word expressions and their accurate identification requires
the user to specify n-gram range, we have proposed a shortcut method to
preserve the structure of the expression based on TF-IDF. In order to evaluate
the pre-processing method and select the concepts, we have designed two types
of downstream tasks (multiple and binary classification) using the capabilities
of transformer-based models. The obtained results show the superiority of
proposed method in combination with SciBERT model, also offer an insight into
the efficacy of general extracting essential phrase methods for clinical notes.

在这项研究中，使用临床自然语言处理技术解决了识别和映射标准术语的问题。提出了一种用于提取临床文本中关键概念的 TF-IDF 快捷方法，并使用基于 transformer 的模型设计了两种下游任务，结果表明 SciBERT 模型在结合提出的方法时具有优越性，并为临床笔记的关键短语提取提供了洞察。

应用无监督关键词方法于从出院记录中提取出的概念

Applying unsupervised keyphrase methods on concepts extracted from discharge sheets

This paper addresses the problem of key phrase extraction from sentences.
Existing state-of-the-art supervised methods require large amounts of annotated
data to achieve good performance and generalization. Collecting labeled data
is, however, often expensive. In this paper, we redefine the problem as
question-answer extraction, and present SAMIE: Self-Asking Model for
Information Ixtraction, a semi-supervised model which dually learns to ask and
to answer questions by itself. Briefly, given a sentence $s$ and an answer $a$,
the model needs to choose the most appropriate question $\hat q$; meanwhile,
for the given sentence $s$ and same question $\hat q$ selected in the previous
step, the model will predict an answer $\hat a$. The model can support few-shot
learning with very limited supervision. It can also be used to perform
clustering analysis when no supervision is provided. Experimental results show
that the proposed method outperforms typical supervised methods especially when
given little labeled data.

本篇论文提出了一种自我抽取问题模型 ——SAMIE (Self-Asking Model for Information Extraction) 以半监督的方式同时学习提问和回答，将关键词抽取问题重新定义为问题 - 回答的抽取问题，使得少量有标注数据就可实现优秀的性能，并可用于聚类分析，结果表明该方法超过了传统的有监督学习方法，特别是在少量标记数据的情况下。

双重问答提取的半监督少样本学习

Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction

Fast and effective automated indexing is critical for search and personalized
services. Key phrases that consist of one or more words and represent the main
concepts of the document are often used for the purpose of indexing. In this
paper, we investigate the use of additional semantic features and
pre-processing steps to improve automatic key phrase extraction. These features
include the use of signal words and freebase categories. Some of these features
lead to significant improvements in the accuracy of the results. We also
experimented with 2 forms of document pre-processing that we call light
filtering and co-reference normalization. Light filtering removes sentences
from the document, which are judged peripheral to its main content.
Co-reference normalization unifies several written forms of the same named
entity into a unique form. We also needed a "Gold Standard" - a set of labeled
documents for training and evaluation. While the subjective nature of key
phrase selection precludes a true "Gold Standard", we used Amazon's Mechanical
Turk service to obtain a useful approximation. Our data indicates that the
biggest improvements in performance were due to shallow semantic features, news
categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of
deeper semantic features such as Freebase sub-categories was not beneficial by
itself, but in combination with pre-processing, did cause slight improvements
in the nDCG scores.

本文研究了使用信号词、Freebase 类别等语义特征和预处理步骤来提高自动关键词抽取的准确性，实验结果表明，浅层的语义特征、新闻类别、修辞信号是提高性能的最主要因素，并且将 Freebase 子类别与预处理结合使用，可略微提高性能。