Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism
Department of Computer and Information Science University of Delaware（特拉华大学计算机与信息科学系）；University of Delaware（特拉华大学）；Department of Computer and Information Science（计算机与信息科学系）
With the explosive growth of biomedical literature, designing automatic tools to extract information from the literature has great significance in biomedical research. Recently, transformer-based BERT models adapted to the biomedical domain have produced leading results. However, all the existing BERT models for relation classification only utilize partial knowledge from the last layer. In this paper, we will investigate the method of utilizing the entire layer in the fine-tuning process of BERT model. To the best of our knowledge, we are the first to explore this method. The experimental results illustrate that our method improves the BERT model performance and outperforms the state-of-the-art methods on three benchmark datasets for different relation extraction tasks. In addition, further analysis indicates that the key knowledge about the relations can be learned from the last layer of BERT model.
Academia Sinica（中央研究院）；National Yang Ming University（国立阳明大学）；National Tsing-Hua University（国立清华大学）；National Central University（国立中央大学）
The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task.…
National Center for Biotechnology Information（国家生物技术信息中心）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference. Our empirical results demonstrate that the MTL fine-tuned models outperform state-of-the-art transformer models (e.g., BERT and its variants) by 2.0% and 1.3% in biomedical and clinical domains, respectively. Pairwise MTL further demonstrates more details a…
Korea University（高丽大学）；Clova AI Research, Naver Corp（Naver公司Clova人工智能研究院）
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 recent…
University of Delaware（特拉华大学）；Weill Cornell Medicine（威尔康奈尔医学院）
Contrastive learning has been used to learn a high-quality representation of the image in computer vision. However, contrastive learning is not widely utilized in natural language processing due to the lack of a general method of data augmentation for text data. In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction. The key knob of our framework is a unique contrastive pre-training step tailored for the relation extraction tasks by seamlessly integrating linguistic knowledge into the data augmentatio…
Sun Yat-sen University（中山大学）；National Institutes of Health（美国国立卫生研究院）；University of Pennsylvania（宾夕法尼亚大学）
Pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art performance in natural language processing (NLP) tasks. Recently, BERT has been adapted to the biomedical domain. Despite the effectiveness, these models have hundreds of millions of parameters and are computationally expensive when applied to large-scale NLP applications. We hypothesized that the number of parameters of the original BERT can be dramatically reduced with minor impact on performance. In this study, we present Bioformer, a compact BERT model for biomed…
Dalian University of Technology（大连理工大学）；Beijing Institute of Health Administration and Medical Information（北京卫生健康经济研究和医学信息研究所）
Recognition of biomedical entities from literature is a challenging research focus, which is the foundation for extracting a large amount of biomedical knowledge existing in unstructured texts into structured formats. Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method. This method, however, often cannot take full advantage of the semantic information in the dataset, and the performance is not always satisfactory. In this work, instead of treating the BioNER task as a sequence labeling problem, we formulate it as a …
Rutgers University–New Brunswick（罗格斯大学新伯朗士威校区）；Hasso Plattner Institute（哈索·普拉特纳研究所）；University of Potsdam（波茨坦大学）
Biomedical entity linking is the task of identifying mentions of biomedical concepts in text documents and mapping them to canonical entities in a target thesaurus. Recent advancements in entity linking using BERT-based models follow a retrieve and rerank paradigm, where the candidate entities are first selected using a retriever model, and then the retrieved candidates are ranked by a reranker model. While this paradigm produces state-of-the-art results, they are slow both at training and test time as they can process only one mention at a time. To mitigate these issues, we propose a BERT-bas…
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国家生物技术信息中心（NCBI）、美国国家医学图书馆（NLM）、美国国立卫生研究院（NIH））
A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation. To automatically extract information from biomedical literature, existing biomedical text-mining approaches typically formulate the problem as a cross-sentence n-ary relation-extraction task that detects relations among n entities across multiple sentences, and use either a graph neural network (GNN) with long short-term memory (LSTM) or an attention mechanism. Recently, Transformer has been shown to outperform LSTM on many natural language…
Manipal Institute of Technology（马尼帕尔理工学院）；Elsevier Labs（爱思唯尔实验室）
Biomedical Named Entity Recognition (NER) is a challenging problem in biomedical information processing due to the widespread ambiguity of out of context terms and extensive lexical variations. Performance on bioNER benchmarks continues to improve due to advances like BERT, GPT, and XLNet. FLAIR (1) is an alternative embedding model which is less computationally intensive than the others mentioned. We test FLAIR and its pretrained PubMed embeddings (which we term BioFLAIR) on a variety of bio NER tasks and compare those with results from BERT-type networks. We also investigate the effects of a…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base. Recently, many BERT-based models have been introduced for the task. While these models have achieved competitive results on many datasets, they are computationally expensive and contain about 110M parameters. Little is known about the factors contributing to their impressive performance and whether the over-parameterization is needed. In this work, we shed some light on the inner working mechanisms of these large BERT-based models. Through a set of probing experim…
Institut Polytechnique de Paris（巴黎综合理工学院）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）
Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word orderings. Recently, BERT-based methods have advanced the state-of-the-art by allowing for rich representations of word sequences. However, they often have hundreds of millions of parameters and require heavy computing resources, which limits their applications in resource-limited s…
Loria, CNRS, Inria, Université de Lorraine（洛里亚研究所、法国国家科学研究中心、法国国家信息与自动化研究所、洛林大学）
Relation extraction (RE) consists in identifying and structuring automatically relations of interest from texts. Recently, BERT improved the top performances for several NLP tasks, including RE. However, the best way to use BERT, within a machine learning architecture, and within a transfer learning strategy is still an open question since it is highly dependent on each specific task and domain. Here, we explore various BERT-based architectures and transfer learning strategies (i.e., frozen or fine-tuned) for the task of biomedical RE on two corpora. Among tested architectures and strategies, …
This paper presents our participation in the AGAC Track from the 2019 BioNLP Open Shared Tasks. We provide a solution for Task 3, which aims to extract "gene - function change - disease" triples, where "gene" and "disease" are mentions of particular genes and diseases respectively and "function change" is one of four pre-defined relationship types. Our system extends BERT (Devlin et al., 2018), a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus and whose parameters can be fine-tuned to solve specific tasks with minimal additional …
This paper presents several BERT-based models for Russian language biomedical text mining (RuBioBERT, RuBioRoBERTa). The models are pre-trained on a corpus of freely available texts in the Russian biomedical domain. With this pre-training, our models demonstrate state-of-the-art results on RuMedBench - Russian medical language understanding benchmark that covers a diverse set of tasks, including text classification, question answering, natural language inference, and named entity recognition.
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO_2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on target-domain text and align the resulting word vectors with the wordpiece vectors of a general-domain PTLM. We evaluate on eight biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model. We cover over 60% of the BioBERT-BERT F1 delta, at 5% of BioBERT's CO_2 footprin…
Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs. We developed a set of three activities introducing introductory BMI students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT. These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment. This disease knowledge is critical for many health-related and biomedical tasks, including consumer health question answering, medical language inference and disease name recognition. While pre-trained language models like BERT have shown success in capturing syntactic, semantic, and world knowledge from text, we find they can be further complemented by specific information like knowledge of symptoms, diagnoses, treatments, and other disease aspects. Hence, we int…
In this work we present Google's submission to the BioASQ 7 biomedical question answering (QA) task (specifically Task 7b, Phase B). The core of our systems are based on BERT QA models, specifically the model of \cite{alberti2019bert}. In this report, and via our submissions, we aimed to investigate two research questions. We start by studying how domain portable are QA systems that have been pre-trained and fine-tuned on general texts, e.g., Wikipedia. We measure this via two submissions. The first is a non-adapted model that uses a public pre-trained BERT model and is fine-tuned on the Natur…
Information Retrieval (IR) is the task of obtaining pieces of data (such as documents) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstream Natural Language Processing (NLP) tasks. Practically, IR is at the heart of many widely-used technologies like search engines. While probabilistic ranking functions like the Okapi BM25 function have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidi…
Dalian University of Technology（大连理工大学）；Beijing Institute of Health Administration and Medical Information（北京卫生健康管理与医学信息研究所）
Motivation: The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction. Results: In this paper, we propose a novel neural network-based approach …
Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans. Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the ever increasing biomedical literature poses unique challenges. We propose BBA…
The Ohio State University（俄亥俄州立大学）；National Library of Medicine（美国国家医学图书馆）；AI Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；George Washington University（乔治华盛顿大学）
The current UMLS (Unified Medical Language System) Metathesaurus construction process for integrating over 200 biomedical source vocabularies is expensive and error-prone as it relies on the lexical algorithms and human editors for deciding if the two biomedical terms are synonymous. Recent advances in Natural Language Processing such as Transformer models like BERT and its biomedical variants with contextualized word embeddings have achieved state-of-the-art (SOTA) performance on downstream tasks. We aim to validate if these approaches using the BERT models can actually outperform the existin…
Case Western University（凯斯西储大学）；National Cancer Institute（国家癌症研究所）；Vietnam National University（越南国家大学）；University of Science, Vietnam National University（越南国家大学科学大学）
In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora. Our model outperforms the current SOTA methods (i.e. BERT, BioBERT, Base T5) on tasks in named entity relation, relation extraction, natural language inference, and question-answering. We show that text-generation methods have significant potential in a broad array of biomedical NLP tasks, particularly those requiring longer, more complex outputs. Our results support the exploration of more difficult text generation tasks and the development of new methods in this area
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly red…
National Center for Biotechnology Information（国家生物技术信息中心）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We m…
University of Cambridge（剑桥大学）；Monash University（莫纳什大学）
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach that can handle a very large knowledge graph (KG) by partitioning it into smaller sub-graphs and infusing their specific knowledge into various BERT models using lightweight adapters. To leverage the overall factual knowledge for a target task, these sub-graph adapters are further fine-tuned along with the underlying BERT through a mixture layer. We evaluate our MoP with three biomedical BERTs (SciBERT, BioBERT, Pubme…
In this paper, we detail our submission to the 2019, 7th year, BioASQ competition. We present our approach for Task-7b, Phase B, Exact Answering Task. These Question Answering (QA) tasks include Factoid, Yes/No, List Type Question answering. Our system is based on a contextual word embedding model. We have used a Bidirectional Encoder Representations from Transformers(BERT) based system, fined tuned for biomedical question answering task using BioBERT. In the third test batch set, our system achieved the highest MRR score for Factoid Question Answering task. Also, for List type question answer…
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；University of Delaware（特拉华大学）
Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e.g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus with multiple entity types (e.g., gene/protein, diseas…
This paper presents Macquarie University's participation to the BioASQ Synergy Task, and BioASQ9b Phase B. In each of these tasks, our participation focused on the use of query-focused extractive summarisation to obtain the ideal answers to medical questions. The Synergy Task is an end-to-end question answering task on COVID-19 where systems are required to return relevant documents, snippets, and answers to a given question. Given the absence of training data, we used a query-focused summarisation system that was trained with the BioASQ8b training data set and we experimented with methods to …
Athens University of Economics and Business（雅典经济与商业大学）；Research Center ‘Athena’（“雅典娜”研究中心）；Google Research（谷歌研究院）
We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset, and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-bas…
This paper explores whether the use of drug reviews and social media could be leveraged as potential alternative sources for pharmacovigilance of adverse drug reactions (ADRs). We examined the performance of BERT alongside two variants that are trained on biomedical papers, BioBERT7, and clinical notes, Clinical BERT8. A variety of 8 different BERT models were fine-tuned and compared across three different tasks in order to evaluate their relative performance to one another in the ADR tasks. The tasks include sentiment classification of drug reviews, presence of ADR in twitter postings, and na…
RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
Department of CSE Emory University Georgia, USA（计算机科学与工程系 埃默里大学 美国佐治亚州）；Department of CSE SRM University AP Andhra Pradesh, India（计算机科学与工程系 SRM大学AP 安得拉邦印度）；Department of CSE Trine University Michigan, USA（计算机科学与工程系 特林大学 密歇根州美国）
PrionNER: A Named Entity Recognition Dataset for Prion Disease Biomedical Literature
The University of Tokyo（东京大学）；Medical Doctor, Independent Researcher（医学博士，独立研究员）；Center for Language AI Research, Tohoku University（语言人工智能研究中心，东北大学）；RIKEN Center for Advanced Intelligence Project（日本理化学研究院高级智能项目中心）；National Institute of Informatics（信息机构国家研究所）
The success of pre-trained word embeddings has motivated its use in tasks in the biomedical domain. The BERT language model has shown remarkable results on standard performance metrics in tasks such as Named Entity Recognition (NER) and Semantic Textual Similarity (STS), which has brought significant progress in the field of NLP. However, it is unclear whether these systems work seemingly well in critical domains, such as legal or medical. For that reason, in this work, we propose an adversarial evaluation scheme on two well-known datasets for medical NER and STS. We propose two types of attac…
The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP
School of Computation, Information and Technology（计算信息科技学院）；Technical University of Munich（慕尼黑技术大学）；Chair of IT Infrastructure for Translational Medical Research（转化医学研究信息基础设施主任）；Faculty of Applied Computer Science（应用计算机科学学院）；University of Augsburg（艾希施泰特大学）；Institute of General Practice（基础医学研究所）；Faculty of Medicine and Medical Center（医学学院和医学中心）
In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical texts. However, construction and maintenance of domain knowledge bases are resource-intense tasks requiring significant manual annotation. In this paper, we demonstrate that contextualized representations extracted from the pre-trained deep language model BERT, can be effectively used to measure the similarity between sentences and to quantify the informative content. The results show that our BERT-based summarizer c…
AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院西德尼·金梅尔综合癌症中心）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
Objective: This work aimed to demonstrate the effectiveness of a hybrid approach based on Sentence BERT model and retrofitting algorithm to compute relatedness between any two biomedical concepts. Materials and Methods: We generated concept vectors by encoding concept preferred terms using ELMo, BERT, and Sentence BERT models. We used BioELMo and Clinical ELMo. We used Ontology Knowledge Free (OKF) models like PubMedBERT, BioBERT, BioClinicalBERT, and Ontology Knowledge Injected (OKI) models like SapBERT, CoderBERT, KbBERT, and UmlsBERT. We trained all the BERT models using Siamese network on …
