scholarly journals Transfer learning for biomedical named entity recognition with neural networks

2018 ◽  
Author(s):  
John M Giorgi ◽  
Gary D Bader
Keyword(s):  
Transfer Learning ◽  
Short Term Memory ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Supplementary Information ◽  
Link Type ◽  
Training Examples

AbstractMotivationThe explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases, and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER.ResultsWe demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target data sets with a small number of labels (approximately 6000 or less).Availability and implementationSource code for the LSTM-CRF is available at https://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available at https://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Towards reliable named entity recognition in the biomedical domain

2019 ◽  
Vol 36 (1) ◽  
pp. 280-286 ◽  
Author(s):  
John M Giorgi ◽  
Gary D Bader
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Supplementary Information ◽  
Maximal Increase ◽  
Named Entity ◽  
Task Learning

Abstract Motivation Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction. For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs), with a recent focus on deep learning. However, recent work has suggested that the high performance of CRFs for BioNER may not generalize to corpora other than the one it was trained on. In our analysis, we find that a popular deep learning-based approach to BioNER, known as bidirectional long short-term memory network-conditional random field (BiLSTM-CRF), is correspondingly poor at generalizing. To address this, we evaluate three modifications of BiLSTM-CRF for BioNER to improve generalization: improved regularization via variational dropout, transfer learning and multi-task learning. Results We measure the effect that each strategy has when training/testing on the same corpus (‘in-corpus’ performance) and when training on one corpus and evaluating on another (‘out-of-corpus’ performance), our measure of the model’s ability to generalize. We found that variational dropout improves out-of-corpus performance by an average of 4.62%, transfer learning by 6.48% and multi-task learning by 8.42%. The maximal increase we identified combines multi-task learning and variational dropout, which boosts out-of-corpus performance by 10.75%. Furthermore, we make available a new open-source tool, called Saber that implements our best BioNER models. Availability and implementation Source code for our biomedical IE tool is available at https://github.com/BaderLab/saber. Corpora and other resources used in this study are available at https://github.com/BaderLab/Towards-reliable-BioNER. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Towards reliable named entity recognition in the biomedical domain

2019 ◽  
Author(s):  
John Giorgi ◽  
Gary Bader
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
High Performance ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Maximal Increase ◽  
Named Entity ◽  
Task Learning

Motivation: Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction (IE). For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs), with a recent focus on deep learning. However, recent work has suggested that the high performance of CRFs for BioNER may not generalize to corpora other than the one it was trained on. In our analysis, we find that a popular deep learning-based approach to BioNER, known as bidirectional long short-term memory network-conditional random field (BiLSTM-CRF), is correspondingly poor at generalizing - often dramatically overfitting the corpus it was trained on. To address this, we evaluate three modifications of BiLSTM-CRF for BioNER to alleviate overfitting and improve generalization: improved regularization via variational dropout, transfer learning, and multi-task learning. Results: We measure the effect that each strategy has when training/testing on the same corpus ("in-corpus" performance) and when training on one corpus and evaluating on another ("out-of-corpus" performance), our measure of the models ability to generalize. We found that variational dropout improves out-of-corpus performance by an average of 4.62%, transfer learning by 6.48% and multi-task learning by 8.42%. The maximal increase we identified combines multi-task learning and variational dropout, which boosts out-of-corpus performance by 10.75%. Furthermore, we make available a new open-source tool, called Saber, that implements our best BioNER models. Availability: Source code for our biomedical IE tool is available at https://github.com/BaderLab/saber. Corpora and other resources used in this study are available at https://github.com/BaderLab/Towards- reliable-BioNER.

scholarly journals CWPC_BiAtt: Character–Word–Position Combined BiLSTM-Attention for Chinese Named Entity Recognition

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 45 ◽  
Author(s):  
Shardrom Johnson ◽  
Sherlock Shen ◽  
Yuanchen Liu
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Attention Mechanism ◽  
Entity Recognition ◽  
Position Information ◽  
Named Entity ◽  
Pos Tagging ◽  
Word Position

Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Character–Word–Position Combined BiLSTM-Attention (CWPC_BiAtt) for the Chinese NER task. Comprehensive-embedding via the Bidirectional Llong Short-Term Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Position-embedding in comprehensive-embedding can compensate for attention-mechanism to provide position information for the disordered sequence, which shows that comprehensive-embedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest F-score, achieving the state-of-the-art performance in the MSRA dataset and Weibo NER corpus.

scholarly journals Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

2019 ◽  
Vol 9 (18) ◽  
pp. 3658 ◽  
Author(s):  
Jianliang Yang ◽  
Yuenan Liu ◽  
Minghui Qian ◽  
Chenghua Guan ◽  
Xiangfei Yuan
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.

Dataset-aware multi-task learning approaches for biomedical named entity recognition

2020 ◽  
Vol 36 (15) ◽  
pp. 4331-4338
Author(s):  
Mei Zuo ◽  
Yang Zhang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Quality Data ◽  
Supplementary Information ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition

Abstract Motivation Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets. Results We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another. Availability and implementation Our source code is available at https://github.com/zmmzGitHub/MTL-BC-LBC-BioNER and all datasets are publicly available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MasakhaNER: Named Entity Recognition for African Languages

2021 ◽  
Vol 9 ◽  
pp. 1116-1131
Author(s):  
David Ifeoluwa Adelani ◽  
Jade Abbott ◽  
Graham Neubig ◽  
Daniel D’souza ◽  
Julia Kreutzer ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Future Research ◽  
African Continent ◽  
High Quality ◽  
African Languages ◽  
Named Entity

Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

scholarly journals Pengenalan Entitas Bernama Otomatis untuk Bahasa Indonesia dengan Pendekatan Pembelajaran Mesin

2018 ◽  
Author(s):  
Yudi Wibisono ◽  
Masayu Leylia Khodra
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Named Entity ◽  
Long Short Term Memory ◽  
F Measure ◽  
Bahasa Indonesia

Pengenalan entitas bernama (named-entity recognition atau NER) adalah proses otomatis mengekstraksi entitas bernama yang dianggap penting di dalam sebuah teks dan menentukan kategorinya ke dalam kategori terdefinisi. Sebagai contoh, untuk teks berita, NER dapat mengekstraksi nama orang, nama organisasi, dan nama lokasi. NER bermanfaat dalam berbagai aplikasi analisis teks, misalnya pencarian, sistem tanya jawab, peringkasan teks dan mesin penerjemah. Tantangan utama NER adalah penanganan ambiguitas makna karena konteks kata pada kalimat, misalnya kata “Cendana” dapat merupakan nama lokasi (Jalan Cendana), atau nama organisasi (Keluarga Cendana), atau nama tanaman. Tantangan lainnya adalah penentuan batas entitas, misalnya “[Istora Senayan] [Jakarta]”. Berbagai kakas NER telah dikembangkan untuk berbagai bahasa terutama Bahasa Inggris dengan kinerja yang baik, tetapi kakas NER bahasa Indonesia masih memiliki kinerja yang belum baik. Makalah ini membahas pendekatan berbasis pembelajaran mesin untuk menghasilkan model NER bahasa Indonesia. Pendekatan ini sangat bergantung pada korpus yang menjadi sumber belajar, dan teknik pembelajaran mesin yang digunakan. Teknik yang akan digunakan adalah LSTM - CRF (Long Short Term Memory – Conditional Random Field). Hasil terbaik (F-measure = 0.72) didapatkan dengan menggunakan word embedding GloVe Wikipedia Bahasa Indonesia.

scholarly journals Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach

2019 ◽  
Vol 16 (19) ◽  
pp. 3628 ◽  
Author(s):  
Erdenebileg Batbaatar ◽  
Keun Ho Ryu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Adverse Drug Events ◽  
Viterbi Algorithm ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Health Related

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets.

scholarly journals Applying Citizen Science to Gene, Drug, and Disease Relationship Extraction from Biomedical Abstracts

2019 ◽  
Author(s):  
Ginger Tsueng ◽  
Max Nanis ◽  
Jennifer T Fouquier ◽  
Michael Mayers ◽  
Benjamin M Good ◽  
...  
Keyword(s):  
Information Extraction ◽  
Citizen Science ◽  
Computational Methods ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Supplementary Information ◽  
Named Entity ◽  
Relationship Extraction

Abstract Motivation Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depends on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction. Results In this paper, we introduce the Relationship Extraction Module of the web-based application Mark2Cure and demonstrate that citizen scientists can perform relationship extraction. We confirm the importance of accurate named entity recognition on user performance of relationship extraction and identify design issues that impacted data quality. We find that the data generated by citizen scientists can be used to identify relationship types not currently available in the Mark2Cure Relationship Extraction Module. We compare the citizen science-generated data with algorithm-mined data and identify ways in which the two approaches may complement one another. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration, and natural language processing. Availability Mark2Cure platform: https://mark2cure.org. Mark2Cure source code: https://github.com/sulab/mark2cure Data and analysis code for this paper: https://github.com/gtsueng/M2C_rel_nb Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition

2021 ◽  
Vol 11 (18) ◽  
pp. 8682
Author(s):  
Ching-Sheng Lin ◽  
Jung-Sing Jwo ◽  
Cheng-Hsiung Lee
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Neural Model ◽  
Entity Recognition ◽  
Chinese Word Segmentation ◽  
Named Entities ◽  
Named Entity ◽  
Biomedical Systems ◽  
N Gram

Clinical Named Entity Recognition (CNER) focuses on locating named entities in electronic medical records (EMRs) and the obtained results play an important role in the development of intelligent biomedical systems. In addition to the research in alphabetic languages, the study of non-alphabetic languages has attracted considerable attention as well. In this paper, a neural model is proposed to address the extraction of entities from EMRs written in Chinese. To avoid erroneous noise being caused by the Chinese word segmentation, we employ the character embeddings as the only feature without extra resources. In our model, concatenated n-gram character embeddings are used to represent the context semantics. The self-attention mechanism is then applied to model long-range dependencies of embeddings. The concatenation of the new representations obtained by the attention module is taken as the input to bidirectional long short-term memory (BiLSTM), followed by a conditional random field (CRF) layer to extract entities. The empirical study is conducted on the CCKS-2017 Shared Task 2 dataset to evaluate our method and the experimental results show that our model outperforms other approaches.

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