Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts.

虽然罕见疾病的特征在于患病率低，但大约3亿人受到罕见疾病的影响。对这些条件的早期和准确诊断是一般从业者的主要挑战，没有足够的知识来识别它们。除此之外，罕见疾病通常会显示各种表现形式，这可能会使诊断更加困难。延迟的诊断可能会对患者的生命产生负面影响。因此，迫切需要增加关于稀有疾病的科学和医学知识。自然语言处理（NLP）和深度学习可以帮助提取有关罕见疾病的相关信息，以促进其诊断和治疗。本文探讨了几种深度学习技术，例如双向长期内存（BILSTM）网络或基于来自变压器（BERT）的双向编码器表示的深层语境化词表示，以识别罕见疾病及其临床表现（症状和症状） Raredis语料库。该毒品含有超过5,000名罕见疾病和近6,000个临床表现。 Biobert，基于BERT和培训的生物医学Corpora培训的域特定语言表示，获得了最佳结果。特别是，该模型获得罕见疾病的F1分数为85.2％，表现优于所有其他模型。

Many prior language modeling efforts have shown that pre-training on an in-domain corpus can significantly improve performance on downstream domain-specific NLP tasks. However, the difficulties associated with collecting enough in-domain data might discourage researchers from approaching this pre-training task. In this paper, we conducted a series of experiments by pre-training Bidirectional Encoder Representations from Transformers (BERT) with different sizes of biomedical corpora. The results demonstrate that pre-training on a relatively small amount of in-domain data (4GB) with limited training steps, can lead to better performance on downstream domain-specific NLP tasks compared with fine-tuning models pre-trained on general corpora.

问题答案（QA）是自然语言处理中最具挑战性的最具挑战性的问题之一（NLP）。问答（QA）系统试图为给定问题产生答案。这些答案可以从非结构化或结构化文本生成。因此，QA被认为是可以用于评估文本了解系统的重要研究区域。大量的QA研究致力于英语语言，调查最先进的技术和实现最先进的结果。然而，由于阿拉伯QA中的研究努力和缺乏大型基准数据集，在阿拉伯语问答进展中的研究努力得到了很大速度的速度。最近许多预先接受的语言模型在许多阿拉伯语NLP问题中提供了高性能。在这项工作中，我们使用四个阅读理解数据集来评估阿拉伯QA的最先进的接种变压器模型，它是阿拉伯语 - 队，ArcD，AQAD和TYDIQA-GoldP数据集。我们微调并比较了Arabertv2基础模型，ArabertV0.2大型型号和ARAElectra模型的性能。在最后，我们提供了一个分析，了解和解释某些型号获得的低绩效结果。

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a;Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

当前在提取问题答案（EQA）中进行的研究对单跨度提取设置进行了建模，其中单个答案跨度是可以预测给定问题对对的标签。对于通用域EQA来说，这种设置是自然的，因为可以单个跨度可以回答通用域中的大多数问题。遵循通用域EQA模型，当前的生物医学EQA（BIOEQA）模型利用单跨度提取设置，采用后处理步骤。在本文中，我们调查了整个普通和生物医学领域的问题分布，发现生物医学问题更可能需要列表型答案（多个答案），而不是Factoid-type答案（单个答案）。这需要能够为问题提供多个答案的模型。基于这项初步研究，我们为Bioeqa提出了一种序列标记方法，Bioeqa是一种多跨度提取设置。我们的方法直接以不同数量的短语作为答案来解决问题，并可以学会从培训数据中确定问题的答案数量。我们在BioASQ 7B和8B列表类型问题上的实验结果优于表现最佳的现有模型，而无需进行后处理步骤。源代码和资源可免费下载，网址为https://github.com/dmis-lab/seqtagqa

与生物医学命名实体识别任务有关的挑战是：现有方法考虑了较少数量的生物医学实体（例如疾病，症状，蛋白质，基因）；这些方法不考虑健康的社会决定因素（年龄，性别，就业，种族），这是与患者健康有关的非医学因素。我们提出了一条机器学习管道，该管道通过以下方式改善了以前的努力：首先，它认识到标准类型以外的许多生物医学实体类型；其次，它考虑了与患者健康有关的非临床因素。该管道还包括阶段，例如预处理，令牌化，映射嵌入查找和命名实体识别任务，以从自由文本中提取生物医学命名实体。我们提出了一个新的数据集，我们通过策划COVID-19案例报告来准备。所提出的方法的表现优于五个基准数据集上的基线方法，其宏观和微平均F1得分约为90，而我们的数据集则分别为95.25和93.18的宏观和微平均F1得分。

Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g., gene or disease). We therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to improve generalization. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g., the entire PubMed data).

在生物医学语料库中预先培训的语言模型，例如Biobert，最近在下游生物医学任务上显示出令人鼓舞的结果。另一方面，由于嵌入尺寸，隐藏尺寸和层数等因素，许多现有的预训练模型在资源密集型和计算上都是沉重的。自然语言处理（NLP）社区已经制定了许多策略来压缩这些模型，利用修剪，定量和知识蒸馏等技术，从而导致模型更快，更小，随后更易于使用。同样，在本文中，我们介绍了六种轻型模型，即Biodistilbert，Biotinybert，BioMobilebert，Distilbiobert，Tinybiobert和Cmpactactbiobert，并通过掩护的语言在PubMed DataSet上通过掩护数据进行了知识蒸馏而获得的知识蒸馏来获得。建模（MLM）目标。我们在三个生物医学任务上评估了所有模型，并将它们与Biobert-V1.1进行比较，以创建有效的轻量级模型，以与较大的对应物相同。所有模型将在我们的HuggingFace配置文件上公开可用，网址为https://huggingface.co/nlpie，用于运行实验的代码将在https://github.com/nlpie-research/compact-compact-biomedical-transformers上获得。

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on three common clinical NLP tasks as compared to nonspecific embeddings. These domainspecific models are not as performant on two clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.

Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SCIBERT, a pretrained language model based on BERT (Devlin et al., 2019) to address the lack of high-quality, large-scale labeled scientific data.SCIBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-theart results on several of these tasks. The code and pretrained models are available at https://github.com/allenai/scibert/.

与伯特（Bert）等语言模型相比，已证明知识增强语言表示的预培训模型在知识基础构建任务（即〜关系提取）中更有效。这些知识增强的语言模型将知识纳入预训练中，以生成实体或关系的表示。但是，现有方法通常用单独的嵌入表示每个实体。结果，这些方法难以代表播出的实体和大量参数，在其基础代币模型之上（即〜变压器），必须使用，并且可以处理的实体数量为由于内存限制，实践限制。此外，现有模型仍然难以同时代表实体和关系。为了解决这些问题，我们提出了一个新的预培训模型，该模型分别从图书中学习实体和关系的表示形式，并分别在文本中跨越跨度。通过使用SPAN模块有效地编码跨度，我们的模型可以代表实体及其关系，但所需的参数比现有模型更少。我们通过从Wikipedia中提取的知识图对我们的模型进行了预训练，并在广泛的监督和无监督的信息提取任务上进行了测试。结果表明，我们的模型比基线学习对实体和关系的表现更好，而在监督的设置中，微调我们的模型始终优于罗伯塔，并在信息提取任务上取得了竞争成果。

预先训练的上下文化文本表示模型学习自然语言的有效表示，以使IT机器可以理解。在注意机制的突破之后，已经提出了新一代预磨模的模型，以便自变压器引入以来实现了良好的性能。来自变压器（BERT）的双向编码器表示已成为语言理解的最先进的模型。尽管取得了成功，但大多数可用的型号已经在印度欧洲语言中培训，但是对代表性的语言和方言的类似研究仍然稀疏。在本文中，我们调查了培训基于单语言变换器的语言模型的可行性，以获得代表语言的特定重点是突尼斯方言。我们评估了我们的语言模型对情感分析任务，方言识别任务和阅读理解问答任务。我们表明使用嘈杂的Web爬网数据而不是结构化数据（维基百科，文章等）更方便这些非标准化语言。此外，结果表明，相对小的Web爬网数据集导致与使用较大数据集获得的那些表现相同的性能。最后，我们在所有三个下游任务中达到或改善了最先进的Tunbert模型。我们释放出Tunbert净化模型和用于微调的数据集。

In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The source code and experiment details of this paper can be obtained from https:// github.com/thunlp/ERNIE.

社会科学的学术文献是记录人类文明并研究人类社会问题的文献。随着这种文献的大规模增长，快速找到有关相关问题的现有研究的方法已成为对研究人员的紧迫需求。先前的研究，例如SCIBERT，已经表明，使用特定领域的文本进行预训练可以改善这些领域中自然语言处理任务的性能。但是，没有针对社会科学的预训练的语言模型，因此本文提出了关于社会科学引文指数（SSCI）期刊上许多摘要的预培训模型。这些模型可在GitHub（https://github.com/s-t-full-text-knowledge-mining/ssci-bert）上获得，在学科分类和带有社会科学文学的抽象结构 - 功能识别任务方面表现出色。

自然语言处理领域（NLP）最近看到使用预先接受训练的语言模型来解决几乎任何任务的大量变化。尽管对各种任务的基准数据集显示了很大的改进，但这些模型通常在非标准域中对临床领域的临床域进行次优，其中观察到预训练文件和目标文件之间的巨大差距。在本文中，我们的目标是通过对语言模型的域特定培训结束这种差距，我们调查其对多种下游任务和设置的影响。我们介绍了预先训练的Clin-X（临床XLM-R）语言模型，并展示了Clin-X如何通过两种语言的十个临床概念提取任务的大幅度优于其他预先训练的变压器模型。此外，我们展示了如何通过基于随机分裂和交叉句子上下文的集合来利用我们所提出的任务和语言 - 无人机模型架构进一步改善变压器模型。我们在低资源和转移设置中的研究显​​示，尽管只有250个标记的句子，但在只有250个标记的句子时，缺乏带注释数据的稳定模型表现。我们的结果突出了专业语言模型作为非标准域中的概念提取的Clin-X的重要性，但也表明我们的任务 - 无人机模型架构跨越测试任务和语言是强大的，以便域名或任务特定的适应不需要。 Clin-Xlanguage模型和用于微调和传输模型的源代码在https://github.com/boschresearch/clin\_x/和Huggingface模型集线器上公开使用。

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model - GatorTron - using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on 5 clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve 5 clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.

自从引进原始伯特（即，基础BERT）以来，研究人员通过利用转让学习的好处，开发了各种定制的伯特模型，并通过利用转移学习的好处来提高特定领域和任务的性能。由于数学文本的性质，这通常使用域特定的词汇以及方程和数学符号，我们对数学的新BERT模型的开发对于许多数学下游任务有用。在这个资源论文中，我们介绍了我们的多体制努力（即，美国的两个学习平台和三个学术机构）对此需求：Mathbert，通过在大型数学语料库上预先培训基础伯爵模型来创建的模型预先幼儿园（Pre-K），高中，大学毕业生水平数学内容。此外，我们选择了三个通常用于数学教育的一般NLP任务：知识组件预测，自动分级开放式Q＆A，以及知识追踪，以展示Mathbert对底座的优越性。我们的实验表明，Mathbert以此任务的2-8％达到了1.2-22％，碱基贝尔以前最佳方法。此外，我们建立了一个数学特定的词汇“Mathvocab”，用Mathbert训练。我们发现Mathbert预先接受过的“Mathvocab”优于Mathbert培训的底座伯特词汇（即'Origvocab'）。 Mathbert目前正在参加倾斜平台采用：Stride，Inc，商业教育资源提供商和Accortments.org，是一个免费在线教育平台。我们发布Mathbert以获取公共用途：https://github.com/tbs17/mathbert。

在法律文本中预先培训的基于变压器的预训练语言模型（PLM）的出现，法律领域中的自然语言处理受益匪浅。有经过欧洲和美国法律文本的PLM，最著名的是Legalbert。但是，随着印度法律文件的NLP申请量的迅速增加以及印度法律文本的区别特征，也有必要在印度法律文本上预先培训LMS。在这项工作中，我们在大量的印度法律文件中介绍了基于变压器的PLM。我们还将这些PLM应用于印度法律文件的几个基准法律NLP任务，即从事实，法院判决的语义细分和法院判决预测中的法律法规识别。我们的实验证明了这项工作中开发的印度特定PLM的实用性。