Yidu-S4K 数据集源自CCKS 2019 评测任务一，即“面向中文电子病历的命名实体识别”的数据集，包括两个子任务：
1）医疗命名实体识别：由于国内没有公开可获得的面向中文电子病历医疗实体识别数据集，本年度保留了医疗命名实体识别任务，对2017年度数据集做了修订，并随任务一同发布。本子任务的数据集包括训练集和测试集。
2）医疗实体及属性抽取（跨院迁移）：在医疗实体识别的基础上，对预定义实体属性进行抽取。本任务为迁移学习任务，即在只提供目标场景少量标注数据的情况下，通过其他场景的标注数据及非标注数据进行目标场景的识别任务。本子任务的数据集包括训练集（非目标场景和目标场景的标注数据、各个场景的非标注数据）和测试集（目标场景的标注数据）。
数据集地址：http://openkg.cn/dataset/yidu-s4k
数据描述：Yidu-N4K 数据集源自CHIP 2019 评测任务一，即“临床术语标准化任务”的数据集。
临床术语标准化任务是医学统计中不可或缺的一项任务。临床上，关于同一种诊断、手术、药品、检查、化验、症状等往往会有成百上千种不同的写法。标准化（归一）要解决的问题就是为临床上各种不同说法找到对应的标准说法。有了术语标准化的基础，研究人员才可对电子病历进行后续的统计分析。本质上，临床术语标准化任务也是语义相似度匹配任务的一种。但是由于原词表述方式过于多样，单一的匹配模型很难获得很好的效果。
数据集地址：http://openkg.cn/dataset/yidu-n7k
赛题描述：本次大赛旨在通过糖尿病相关的教科书、研究论文来做糖尿病文献挖掘并构建糖尿病知识图谱。参赛选手需要设计高准确率，高效的算法来挑战这一科学难题。第一赛季课题为“基于糖尿病临床指南和研究论文的实体标注构建”，第二赛季课题为“基于糖尿病临床指南和研究论文的实体间关系构建”。
数据集地址：https://tianchi.aliyun.com/competition/entrance/231687/information
数据描述：该数据集由IEEE中一篇论文中提出，名为：Multi-Scale Attentive Interaction Networks for Chinese Medical Question Answer Selection，他是一个面向中文医疗方向的问答数据集，数量级别达10万级。 文件说明：questions.csv：所有的问题及其内容；answers.csv：所有问题的答案；train_candidates.txt， dev_candidates.txt， test_candidates.txt：将上述两个文件进行了拆分。
数据集地址：https://github.com/zhangsheng93/cMedQA2
任务描述：本次比赛是chip2019中的评测任务二，由平安医疗科技主办。本次评测任务的主要目标是针对中文的疾病问答数据，进行病种间的迁移学习。具体而言，给定来自5个不同病种的问句对，要求判定两个句子语义是否相同或者相近。所有语料来自互联网上患者真实的问题，并经过了筛选和人工的意图匹配标注。首页说明了相关数据的格式。
数据集地址：https://www.biendata.xyz/competition/chip2019/ 需注册才能下载
赛制说明：比赛主打疫情相关的呼吸领域的真实数据积累，数据粒度更加细化，判定难度相比多科室文本相似度匹配更高，同时问答数据也更具时效性。本着宁缺毋滥的原则，问题的场地限制在20字以内，形成相对规范的句对。要求选手通过自然语义算法和医学知识识别相似问答和无关的问题。相关数据说明参见比赛网址首页。
数据集地址：https://tianchi.aliyun.com/competition/entrance/231776/information 需注册才能下载
知识图谱简介：CMeKG（Chinese Medical Knowledge Graph）是利用自然语言处理与文本挖掘技术，基于大规模医学文本数据，以人机结合的方式研发的中文医学知识图谱。CMeKG的构建参考了ICD、ATC、SNOMED、MeSH等权威的国际医学标准以及规模庞大、多源异构的临床指南、行业标准、诊疗规范与医学百科等医学文本信息。CMeKG 1.0包括：6310种疾病、19853种药物（西药、中成药、中草药）、1237种诊疗技术及设备的结构化知识描述，涵盖疾病的临床症状、发病部位、药物治疗、手术治疗、鉴别诊断、影像学检查、高危因素、传播途径、多发群体、就诊科室等以及药物的成分、适应症、用法用量、有效期、禁忌证等30余种常见关系类型，CMeKG描述的概念关系实例及属性三元组达100余万。
CMEKG图谱地址：http://cmekg.pcl.ac.cn/
pkuseg 是由北京大学推出的基于论文PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation 的工具包。其简单易用，支持细分领域分词，有效提升了分词准确度。
pkuseg具有如下几个特点： 1.多领域分词。不同于以往的通用中文分词工具，此工具包同时致力于为不同领域的数据提供个性化的预训练模型。根据待分词文本的领域特点，用户可以自由地选择不同的模型。 我们目前支持了新闻领域，网络领域，医药领域，旅游领域，以及混合领域的分词预训练模型。在使用中，如果用户明确待分词的领域，可加载对应的模型进行分词。如果用户无法确定具体领域，推荐使用在混合领域上训练的通用模型。各领域分词样例可参考 example.txt。 2.更高的分词准确率。相比于其他的分词工具包，当使用相同的训练数据和测试数据，pkuseg可以取得更高的分词准确率。 3.支持用户自训练模型。支持用户使用全新的标注数据进行训练。 4.支持词性标注。
项目地址：https://github.com/lancopku/pkuseg-python
awesome_Chinese_medical_NLP Chinese_medical_NLP
医学自然语言处理相关论文汇总，目前主要汇总了ACL2021、NAACL2021、AAAI2021、AAAI2020、ACL2020和EMNLP2020, EMNLP2021等相关会议论文整理后续有时间还会持续更新。
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding
一种逐渐软的多任务和数据增强的医学问题理解方法
论文地址：https://aclanthology.org/2021.acl-long.119/
摘要：
Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval. To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. We first establish an equivalence between the tasks of question summarization and Recognizing Question Entailment (RQE) using their definitions in the medical domain. Based on this equivalence, we propose a data augmentation algorithm to use just one dataset to optimize for both tasks, with a weighted MTL loss. We introduce gradually soft parameter-sharing: a constraint for decoder parameters to be close, that is gradually loosened as we move to the highest layer. We show through ablation studies that our proposed novelties improve performance. Our method outperforms existing MTL methods across 4 datasets of medical question pairs, in ROUGE scores, RQE accuracy and human evaluation. Finally, we show that our method fares better than single-task learning under 4 low-resource settings.
Competence-based Multimodal Curriculum Learning for Medical Report Generation
用于医学报告生成的基于能力的多模式课程学习
论文地址：https://aclanthology.org/2021.acl-long.234/
摘要：
Medical report generation task, which targets to produce long and coherent descriptions of medical images, has attracted growing research interests recently. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias and 2) the limited medical data. To alleviate the data bias and make best use of available data, we propose a Competence-based Multimodal Curriculum Learning framework (CMCL). Specifically, CMCL simulates the learning process of radiologists and optimizes the model in a step by step manner. Firstly, CMCL estimates the difficulty of each training instance and evaluates the competence of current model; Secondly, CMCL selects the most suitable batch of training instances considering current model competence. By iterating above two steps, CMCL can gradually improve the model’s performance. The experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
通过记忆写作：基于层次检索的医学报告生成
论文地址：https://aclanthology.org/2021.acl-long.387/
摘要：
Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
SMedBERT：用于医学文本挖掘的具有结构化语义的知识增强型预训练语言模型
论文地址：https://aclanthology.org/2021.acl-long.457/
摘要：
Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing them to communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, question matching and natural language inference.
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization
用于医学命名实体识别和规范化的端到端渐进式多任务学习框架
论文地址：https://aclanthology.org/2021.acl-long.485/
摘要：
Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks. The mispredicted mentions from NER will directly influence the results of NEN. Therefore, the NER module is the bottleneck of the whole system. Besides, the learnable features for both tasks are beneficial to improving the model performance. To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way. There are three level tasks with progressive difficulty in the framework. The progressive tasks can reduce the error propagation with the incremental task settings which implies the lower level tasks gain the supervised signals other than errors from the higher level tasks to improve their performances. Besides, the context features are exploited to enrich the semantic information of entity mentions extracted by NER. The performance of NEN profits from the enhanced entity mention features. The standard entities from knowledge bases are introduced into the NER module for extracting corresponding entity mentions correctly. The empirical results on two publicly available medical literature datasets demonstrate the superiority of our method over nine typical methods.
A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization
基于神经转移的疾病命名实体识别和归一化联合模型
论文地址：https://aclanthology.org/2021.acl-long.219/
摘要：
Disease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for many downstream applications. Previous studies have demonstrated that joint modeling of the two sub-tasks has superior performance than the pipelined counterpart. Although the neural joint model based on multi-task learning framework has achieved state-of-the-art performance, it suffers from the boundary inconsistency problem due to the separate decoding procedures. Moreover, it ignores the rich information (e.g., the text surface form) of each candidate concept in the vocabulary, which is quite essential for entity normalization. In this work, we propose a neural transition-based joint model to alleviate these two issues. We transform the end-to-end disease recognition and normalization task as an action sequence prediction task, which not only jointly learns the model with shared representations of the input, but also jointly searches the output by state transitions in one search space. Moreover, we introduce attention mechanisms to take advantage of the text surface form of each candidate concept for better normalization performance. Experimental results conducted on two publicly available datasets show the effectiveness of the proposed method.
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
联合生物医学实体和关系提取与知识增强的集体推理
论文地址：https://aclanthology.org/2021.acl-long.488/
摘要：
Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-the-art results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity and relation extraction tasks
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation
基于知识丰富的抽象意义表示的生物医学文献细粒度信息提取
论文地址：https://aclanthology.org/2021.acl-long.489/
摘要：
Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model’s understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking
跨语言生物医学实体链接的学习领域专业表示
论文地址：https://aclanthology.org/2021.acl-short.72/
摘要：
Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL). However, such abundant expert knowledge is available only for a handful of languages (e.g., English). In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task. The scores indicate large gaps to English performance. We then address the challenge of transferring domain-specific knowledge in resource-rich languages to resource-poor ones. To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data. Remarkably, we show that our proposed domain-specific transfer methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language, and without any in-domain parallel data.
On the Generation of Medical Dialogs for COVID-19
关于 COVID-19 医疗对话的生成
论文地址：https://aclanthology.org/2021.acl-short.112/
摘要：
Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets – CovidDialog – (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with general-domain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctor-like, relevant to conversation history, clinically informative and correct. The code and the data are available at https://github.com/UCSD-AI4H/COVID-Dialogue.
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain
生物医学领域隐式话语关系分类的实体增强
论文地址：https://aclanthology.org/2021.acl-short.116/
摘要：
Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.
Attentive Multiview Text Representation for Differential Diagnosis
用于鉴别诊断的注意力集中的多视图文本表示
论文地址：https://aclanthology.org/2021.acl-short.128/
摘要：
We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.
MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain
MedNLI 不是免疫的：临床领域的自然语言推理工件
论文地址：https://aclanthology.org/2021.acl-short.129/
摘要：
Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance (CITATION). We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts (CITATION). We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on the difficult subset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.
The Biomaterials Annotator: a system for ontology-based concept annotation of biomaterials text
论文地址：https://aclanthology.org/2021.sdp-1.5/
Towards BERT-based Automatic ICD Coding: Limitations and Opportunities
论文地址：https://aclanthology.org/2021.bionlp-1.6/
Scalable Few-Shot Learning of Robust Biomedical Name Representations
论文地址：https://aclanthology.org/2021.bionlp-1.3/
Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization
论文地址：https://aclanthology.org/2021.bionlp-1.2/
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus
论文地址：https://aclanthology.org/2021.naacl-main.139/
Self-Alignment Pretraining for Biomedical Entity Representations
论文地址：https://aclanthology.org/2021.naacl-main.334/
Are we there yet? Exploring clinical domain knowledge of BERT models
论文地址：https://aclanthology.org/2021.bionlp-1.5/
Stress Test Evaluation of Biomedical Word Embeddings
论文地址：https://aclanthology.org/2021.bionlp-1.13/
BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
论文地址：https://aclanthology.org/2021.bionlp-1.16/
Improving Biomedical Pretrained Language Models with Knowledge
论文地址：https://aclanthology.org/2021.bionlp-1.20/
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
论文地址：https://aclanthology.org/2021.bionlp-1.21/
ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization
论文地址：https://aclanthology.org/2021.bionlp-1.29/
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
论文地址：https://aclanthology.org/2021.bionlp-1.23/
Are we there yet? Exploring clinical domain knowledge of BERT models
论文地址：https://aclanthology.org/2021.bionlp-1.5/
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction
论文地址：https://aclanthology.org/2021.bionlp-1.1/
Clustering-based Inference for Biomedical Entity Linking
论文地址：https://aclanthology.org/2021.naacl-main.205/
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
论文地址：https://aclanthology.org/2021.bionlp-1.18/
Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts
论文地址：https://aclanthology.org/2021.bionlp-1.19/
BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
论文地址：https://aclanthology.org/2021.bionlp-1.24/
Semi-Supervised Language Models for Identification of Personal Health Experiential from Twitter Data: A Case for Medication Effects
论文地址：https://aclanthology.org/2021.bionlp-1.25/
Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
论文地址：https://aclanthology.org/2021.nlpmc-1.5/
UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model
论文地址：https://aclanthology.org/2021.bionlp-1.36/
IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.35/
Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning
论文地址：https://aclanthology.org/2021.bionlp-1.32/
MNLP at MEDIQA 2021: Fine-Tuning PEGASUS for Consumer Health Question Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.37/
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.38/
Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization
论文地址：https://aclanthology.org/2021.nlpmc-1.2/
paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.10/
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network
论文地址：https://aclanthology.org/2021.naacl-main.156/
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.28/
SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain
论文地址：https://aclanthology.org/2021.bionlp-1.31/
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.34/
emrKBQA: A Clinical Knowledge-Base Question Answering Dataset
论文地址：https://aclanthology.org/2021.bionlp-1.7/
QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.33/
Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain
论文地址：https://aclanthology.org/2021.bionlp-1.8/
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers
论文地址：https://aclanthology.org/2021.bionlp-1.9/
Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions
论文地址：https://aclanthology.org/2021.nlpmc-1.3/
Extracting Appointment Spans from Medical Conversations
论文地址：https://aclanthology.org/2021.nlpmc-1.6/
Building blocks of a task-oriented dialogue system in the healthcare domain
论文地址：https://aclanthology.org/2021.nlpmc-1.7/
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
论文地址：https://aclanthology.org/2021.nlpmc-1.9/
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification
论文地址：https://aclanthology.org/2021.naacl-main.423/
NCUEE-NLP at MEDIQA 2021: Health Question Summarization Using PEGASUS Transformers
论文地址：https://aclanthology.org/2021.bionlp-1.30/
damo_nlp at MEDIQA 2021: Knowledge-based Preprocessing and Coverage-oriented Reranking for Medical Question Summarization
论文地址：https://aclanthology.org/2021.bionlp-1.12/
Word centrality constrained representation for keyphrase extraction
论文地址：https://aclanthology.org/2021.bionlp-1.17/
Contextual explanation rules for neural clinical classifiers
论文地址：https://aclanthology.org/2021.bionlp-1.22/
Context-aware query design combines knowledge and data for efficient reading and reasoning
论文地址：https://aclanthology.org/2021.bionlp-1.26/
Measuring the relative importance of full text sections for information retrieval from scientific literature.
论文地址：https://aclanthology.org/2021.bionlp-1.27/
Automatic Speech-Based Checklist for Medical Simulations
论文地址：https://aclanthology.org/2021.nlpmc-1.4/
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding
论文地址：https://aclanthology.org/2021.nlpmc-1.8/
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models
论文地址：https://aclanthology.org/2021.sdp-1.4/
BDKG at MEDIQA 2021: System Report for the Radiology Report Summarization Task
论文地址：https://aclanthology.org/2021.bionlp-1.11/
Subtype-Aware Unsupervised Domain Adaptation for Medical Diagnosis
论文地址：https://arxiv.org/pdf/2101.00318.pdf
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
论文地址：https://arxiv.org/pdf/2012.11988.pdf
A Lightweight Neural Model for Biomedical Entity Linking
论文地址：https://arxiv.org/pdf/2012.08844.pdf
Automated Lay Language Summarization of Biomedical Scientific Reviews
论文地址：https://arxiv.org/pdf/2012.12573.pdf
MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization
论文地址：https://arxiv.org/pdf/1902.10118.pdf
MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification
论文地址：https://arxiv.org/pdf/2012.09216.pdf
Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision
论文地址：https://aaai.org/ojs/index.php/AAAI/article/view/6236
Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!
论文地址：https://aaai.org/ojs/index.php/AAAI/article/view/6404
Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses
论文地址：https://aaai.org/ojs/index.php/AAAI/article/view/6412
Learning Conceptual-Contextual Embeddings for Medical Text
论文地址：https://aaai.org/ojs/index.php/AAAI/article/view/6504
LATTE: Latent Type Modeling for Biomedical Entity Linking
论文地址：https://aaai.org/ojs/index.php/AAAI/article/view/6526
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.372/
Evaluation of Machine Translation Methods applied to Medical Terminologies
论文地址：https://www.aclweb.org/anthology/2020.louhi-1.7/
A Multilingual Neural Machine Translation Model for Biomedical Data
论文地址：https://www.aclweb.org/anthology/2020.nlpcovid19-2.16/
Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
论文地址：https://www.aclweb.org/anthology/2020.wmt-1.76/
Elhuyar submission to the Biomedical Translation Task 2020 on terminology and abstracts translation
论文地址：https://www.aclweb.org/anthology/2020.wmt-1.87/
Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation
论文地址：https://www.aclweb.org/anthology/2020.wmt-1.89/
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.111/
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.116/
Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts
论文地址：https://www.aclweb.org/anthology/2020.deelio-1.8/
Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings
论文地址：https://www.aclweb.org/anthology/2020.louhi-1.3/
Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.18/
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.165/
COMETA: A Corpus for Medical Entity Linking in the Social Media
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.253/
Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text
论文地址：https://www.aclweb.org/anthology/2020.louhi-1.2/
BioMegatron: Larger Biomedical Domain Language Model
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.379/
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.17/
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.134/
On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining
论文地址：https://www.aclweb.org/anthology/2020.sdp-1.12/
Biomedical Event Extraction as Sequence Labeling
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.431/
Biomedical Event Extraction with Hierarchical Knowledge Graphs
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.114/
COMETA: A Corpus for Medical Entity Linking in the Social Media
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.253/
MedDialog: Large-scale Medical Dialogue Datasets
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.743/
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/
MedICaT: A Dataset of Medical Images, Captions, and Textual References
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.191/
GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines
论文地址：https://www.aclweb.org/anthology/2020.louhi-1.5/
Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.5/
Classification of Syncope Cases in Norwegian Medical Records
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.9/
Weakly Supervised Medication Regimen Extraction from Medical Conversations
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.20/
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.335/
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.202/
Generating Accurate Electronic Health Assessment from Medical Graph
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.336/
Biomedical Event Extraction as Multi-turn Question Answering
论文地址：https://www.aclweb.org/anthology/2020.louhi-1.10/
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities
论文地址：https://www.aclweb.org/anthology/2020.nlpcovid19-2.20/
Developing a Curated Topic Model for COVID-19 Medical Research Literature
论文地址：https://www.aclweb.org/anthology/2020.nlpcovid19-2.30/
ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora
论文地址：https://www.aclweb.org/anthology/2020.sdp-1.15/
Learning Informative Representations of Biomedical Relations with Latent Variable Models
论文地址：https://www.aclweb.org/anthology/2020.sustainlp-1.3/
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
论文地址：https://www.aclweb.org/anthology/2020.clinicalnlp-1.8/
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.2/
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.77/
Characterizing the Value of Information in Medical Notes
论文地址：https://www.aclweb.org/anthology/2020.findings-emnlp.187/
Querying Across Genres for Medical Claims in News
论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.139/
An efficient representation of chronological events in medical texts