Automated medical coding is a process of codifying clinical notes to
appropriate diagnosis and procedure codes automatically from the standard
taxonomies such as ICD (International Classification of Diseases) and CPT
(Current Procedure Terminology). The manual coding process involves the
identification of entities from the clinical notes followed by querying a
commercial or non-commercial medical codes Information Retrieval (IR) system
that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We
propose to automate this manual process by automatically constructing a query
for the IR system using the entities auto-extracted from the clinical notes. We
propose \textbf{GrabQC}, a \textbf{Gra}ph \textbf{b}ased \textbf{Q}uery
\textbf{C}ontextualization method that automatically extracts queries from the
clinical text, contextualizes the queries using a Graph Neural Network (GNN)
model and obtains the ICD Codes using an external IR system. We also propose a
method for labelling the dataset for training the model. We perform experiments
on two datasets of clinical text in three different setups to assert the
effectiveness of our approach. The experimental results show that our proposed
method is better than the compared baselines in all three settings.

研究提出了一个基于图嵌入神经网络的方法自动构建与临床文本有关的查询，并在外部信息检索系统中获取 ICD 编码，通过实验表明该方法的有效性。

GrabQC: 基于图的查询语境化技术实现自动 ICD 编码

GrabQC: Graph based Query Contextualization for automated ICD coding

The core of information retrieval (IR) is to identify relevant information
from large-scale resources and return it as a ranked list to respond to the
user's information need. In recent years, the resurgence of deep learning has
greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural
information retrieval), especially the paradigm of pre-training methods (PTMs).
Owing to sophisticated pre-training objectives and huge model size, pre-trained
models can learn universal language representations from massive textual data,
which are beneficial to the ranking task of IR. Recently, a large number of
works, which are dedicated to the application of PTMs in IR, have been
introduced to promote the retrieval performance. Considering the rapid progress
of this direction, this survey aims to provide a systematic review of
pre-training methods in IR. To be specific, we present an overview of PTMs
applied in different components of an IR system, including the retrieval
component, the re-ranking component, and other components. In addition, we also
introduce PTMs specifically designed for IR, and summarize available datasets
as well as benchmark leaderboards. Moreover, we discuss some open challenges
and highlight several promising directions, with the hope of inspiring and
facilitating more works on these topics for future research.

本综述介绍了信息检索中的预训练方法，特别是神经信息检索，涵盖了预训练方法在不同组件中的应用，可用数据集以及基准排行榜，并讨论了现有挑战和未来研究方向。

信息检索中的预训练方法

Pre-training Methods in Information Retrieval

Neural table-to-text generation models have achieved remarkable progress on
an array of tasks. However, due to the data-hungry nature of neural models,
their performances strongly rely on large-scale training examples, limiting
their applicability in real-world applications. To address this, we propose a
new framework: Prototype-to-Generate (P2G), for table-to-text generation under
the few-shot scenario. The proposed framework utilizes the retrieved
prototypes, which are jointly selected by an IR system and a novel prototype
selector to help the model bridging the structural gap between tables and
texts. Experimental results on three benchmark datasets with three
state-of-the-art models demonstrate that the proposed framework significantly
improves the model performance across various evaluation metrics.

提出了新的框架：Prototype-to-Generate（P2G），旨在通过使用检索的原型来提高神经表生成文本模型在少样本场景下的性能，并利用 IR 系统和新型原型选择器共同选择原型以帮助模型弥合表格和文本之间的结构差距。在三个基准数据集上进行的实验结果表明，该文所提出的框架显著提高了各种评估指标的模型性能。

原型记忆的少样本表格到文本生成

Few-Shot Table-to-Text Generation with Prototype Memory

Parallel data are an important part of a reliable Statistical Machine
Translation (SMT) system. The more of these data are available, the better the
quality of the SMT system. However, for some language pairs such as
Persian-English, parallel sources of this kind are scarce. In this paper, a
bidirectional method is proposed to extract parallel sentences from English and
Persian document aligned Wikipedia. Two machine translation systems are
employed to translate from Persian to English and the reverse after which an IR
system is used to measure the similarity of the translated sentences. Adding
the extracted sentences to the training data of the existing SMT systems is
shown to improve the quality of the translation. Furthermore, the proposed
method slightly outperforms the one-directional approach. The extracted corpus
consists of about 200,000 sentences which have been sorted by their degree of
similarity calculated by the IR system and is freely available for public
access on the Web.

使用双向方法从英语和波斯文档的维基百科中提取平行句子，使用机器翻译系统将波斯文翻译成英文，反之亦然，然后使用 IR 系统测量翻译后句子的相似度，并将提取的句子加入现有 SMT 系统的训练数据，以改善翻译质量。提出的方法略优于单向方法。提取的语料库包含约 200,000 个句子，已按其相似度由 IR 系统计算排序，并可在 Web 上免费访问。