HUNER: improving biomedical NER with pretraining

2019 ◽  
Vol 36 (1) ◽  
pp. 295-302 ◽  
Author(s):  
Leon Weber ◽  
Jannes Münchmeyer ◽  
Tim Rocktäschel ◽  
Maryam Habibi ◽  
Ulf Leser
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
The State ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Supplementary Information ◽  
Named Entity ◽  
Training Development ◽  
The Impact

Abstract Motivation Several recent studies showed that the application of deep neural networks advanced the state-of-the-art in named entity recognition (NER), including biomedical NER. However, the impact on performance and the robustness of improvements crucially depends on the availability of sufficiently large training corpora, which is a problem in the biomedical domain with its often rather small gold standard corpora. Results We evaluate different methods for alleviating the data sparsity problem by pretraining a deep neural network (LSTM-CRF), followed by a rather short fine-tuning phase focusing on a particular corpus. Experiments were performed using 34 different corpora covering five different biomedical entity types, yielding an average increase in F1-score of ∼2 pp compared to learning without pretraining. We experimented both with supervised and semi-supervised pretraining, leading to interesting insights into the precision/recall trade-off. Based on our results, we created the stand-alone NER tool HUNER incorporating fully trained models for five entity types. On the independent CRAFT corpus, which was not used for creating HUNER, it outperforms the state-of-the-art tools GNormPlus and tmChem by 5–13 pp on the entity types chemicals, species and genes. Availability and implementation HUNER is freely available at https://hu-ner.github.io. HUNER comes in containers, making it easy to install and use, and it can be applied off-the-shelf to arbitrary texts. We also provide an integrated tool for obtaining and converting all 34 corpora used in our evaluation, including fixed training, development and test splits to enable fair comparisons in the future. Supplementary information Supplementary data are available at Bioinformatics online.

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.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

2021 ◽  
Vol 2 (4) ◽  
pp. 1-24
Author(s):  
Pratyay Banerjee ◽  
Kuntal Kumar Pal ◽  
Murthy Devarakonda ◽  
Chitta Baral
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Neural Models ◽  
Named Entity ◽  
Input Text ◽  
The Impact ◽  
Biomedical Named Entity Recognition ◽  
Entity Class

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

scholarly journals Chinese named entity recognition: The state of the art

2021 ◽  
Author(s):  
Pan Liu ◽  
Yanming Guo ◽  
Fenglei Wang ◽  
Guohui Li
Keyword(s):  
State Of The Art ◽  
Named Entity Recognition ◽  
The State ◽  
Entity Recognition ◽  
Named Entity

Named-entity recognition for early modern textual documents: a review of capabilities and challenges with strategies for the future

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marco Humbel ◽  
Julianne Nyhan ◽  
Andreas Vlachidis ◽  
Kim Sloan ◽  
Alexandra Ortolja-Baird
Keyword(s):  
Early Modern ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Research Field ◽  
The State ◽  
Entity Recognition ◽  
British Library ◽  
Extensive Literature ◽  
Content Type ◽  
Named Entity

PurposeBy mapping-out the capabilities, challenges and limitations of named-entity recognition (NER), this article aims to synthesise the state of the art of NER in the context of the early modern research field and to inform discussions about the kind of resources, methods and directions that may be pursued to enrich the application of the technique going forward.Design/methodology/approachThrough an extensive literature review, this article maps out the current capabilities, challenges and limitations of NER and establishes the state of the art of the technique in the context of the early modern, digitally augmented research field. It also presents a new case study of NER research undertaken by Enlightenment Architectures: Sir Hans Sloane's Catalogues of his Collections (2016–2021), a Leverhulme funded research project and collaboration between the British Museum and University College London, with contributing expertise from the British Library and the Natural History Museum.FindingsCurrently, it is not possible to benchmark the capabilities of NER as applied to documents of the early modern period. The authors also draw attention to the situated nature of authority files, and current conceptualisations of NER, leading them to the conclusion that more robust reporting and critical analysis of NER approaches and findings is required.Research limitations/implicationsThis article examines NER as applied to early modern textual sources, which are mostly studied by Humanists. As addressed in this article, detailed reporting of NER processes and outcomes is not necessarily valued by the disciplines of the Humanities, with the result that it can be difficult to locate relevant data and metrics in project outputs. The authors have tried to mitigate this by contacting projects discussed in this paper directly, to further verify the details they report here.Practical implicationsThe authors suggest that a forum is needed where tools are evaluated according to community standards. Within the wider NER community, the MUC and ConLL corpora are used for such experimental set-ups and are accompanied by a conference series, and may be seen as a useful model for this. The ultimate nature of such a forum must be discussed with the whole research community of the early modern domain.Social implicationsNER is an algorithmic intervention that transforms data according to certain rules-, patterns- or training data and ultimately affects how the authors interpret the results. The creation, use and promotion of algorithmic technologies like NER is not a neutral process, and neither is their output A more critical understanding of the role and impact of NER on early modern documents and research and focalization of some of the data- and human-centric aspects of NER routines that are currently overlooked are called for in this paper.Originality/valueThis article presents a state of the art snapshot of NER, its applications and potential, in the context of early modern research. It also seeks to inform discussions about the kinds of resources, methods and directions that may be pursued to enrich the application of NER going forward. It draws attention to the situated nature of authority files, and current conceptualisations of NER, and concludes that more robust reporting of NER approaches and findings are urgently required. The Appendix sets out a comprehensive summary of digital tools and resources surveyed in this article.

scholarly journals A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation (Preprint)

2021 ◽  
Author(s):  
Riste Stojanov ◽  
Gorjan Popovski ◽  
Gjorgjina Cenikj ◽  
Barbara Koroušić Seljak ◽  
Tome Eftimov
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Food Science ◽  
Named Entity ◽  
Semantic Resources ◽  
Systematized Nomenclature Of Medicine

BACKGROUND Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only few food semantic resources and few rule-based methods for food information extraction, which often depend on some external resources. However, an annotated corpus with food entities along with their normalization was published in 2019 by using several food semantic resources. OBJECTIVE In this study, we investigated how the recently published bidirectional encoder representations from transformers (BERT) model, which provides state-of-the-art results in information extraction, can be fine-tuned for food information extraction. METHODS We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags. RESULTS All BERT models provided very promising results with 93.30% to 94.31% macro F1 scores in the task of distinguishing food versus nonfood entity, which represents the new state-of-the-art technology in food information extraction. Considering the tasks where semantic tags are predicted, all BERT models obtained very promising results once again, with their macro F1 scores ranging from 73.39% to 78.96%. CONCLUSIONS FoodNER can be used to extract and annotate food entities in 5 different tasks: food versus nonfood entities and distinguishing food entities on the level of food groups by using the closest Hansard semantic tags, the parent Hansard semantic tags, the FoodOn semantic tags, or the Systematized Nomenclature of Medicine Clinical Terms semantic tags.

scholarly journals A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation

10.2196/28229 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e28229
Author(s):  
Riste Stojanov ◽  
Gorjan Popovski ◽  
Gjorgjina Cenikj ◽  
Barbara Koroušić Seljak ◽  
Tome Eftimov
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Food Science ◽  
Named Entity ◽  
Semantic Resources ◽  
Systematized Nomenclature Of Medicine

Background Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only few food semantic resources and few rule-based methods for food information extraction, which often depend on some external resources. However, an annotated corpus with food entities along with their normalization was published in 2019 by using several food semantic resources. Objective In this study, we investigated how the recently published bidirectional encoder representations from transformers (BERT) model, which provides state-of-the-art results in information extraction, can be fine-tuned for food information extraction. Methods We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags. Results All BERT models provided very promising results with 93.30% to 94.31% macro F1 scores in the task of distinguishing food versus nonfood entity, which represents the new state-of-the-art technology in food information extraction. Considering the tasks where semantic tags are predicted, all BERT models obtained very promising results once again, with their macro F1 scores ranging from 73.39% to 78.96%. Conclusions FoodNER can be used to extract and annotate food entities in 5 different tasks: food versus nonfood entities and distinguishing food entities on the level of food groups by using the closest Hansard semantic tags, the parent Hansard semantic tags, the FoodOn semantic tags, or the Systematized Nomenclature of Medicine Clinical Terms semantic tags.

scholarly journals Improving deep learning method for biomedical named entity recognition by using entity definition information

2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Ying Xiong ◽  
Shuai Chen ◽  
Buzhou Tang ◽  
Qingcai Chen ◽  
Xiaolong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
The State ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Learning Methods ◽  
Named Entity ◽  
Sequence Labeling ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information. Material and method We investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score. Results Entity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER. Conclusion Our entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.

scholarly journals HunFlair: an easy-to-use tool for state-of-the-art biomedical named entity recognition

2021 ◽  
Author(s):  
Leon Weber ◽  
Mario Sänger ◽  
Jannes Münchmeyer ◽  
Maryam Habibi ◽  
Ulf Leser ◽  
...  
Keyword(s):  
Operating Systems ◽  
State Of The Art ◽  
Language Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Supplementary Information ◽  
Named Entity ◽  
Text Genre ◽  
Art Research ◽  
Biomedical Named Entity Recognition

Abstract Summary Named entity recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, be highly accurate and be robust toward variations in text genre and style. We present HunFlair, a NER tagger fulfilling these requirements. HunFlair is integrated into the widely used NLP framework Flair, recognizes five biomedical entity types, reaches or overcomes state-of-the-art performance on a wide set of evaluation corpora, and is trained in a cross-corpus setting to avoid corpus-specific bias. Technically, it uses a character-level language model pretrained on roughly 24 million biomedical abstracts and three million full texts. It outperforms other off-the-shelf biomedical NER tools with an average gain of 7.26 pp over the next best tool in a cross-corpus setting and achieves on-par results with state-of-the-art research prototypes in in-corpus experiments. HunFlair can be installed with a single command and is applied with only four lines of code. Furthermore, it is accompanied by harmonized versions of 23 biomedical NER corpora. Availability and implementation HunFlair ist freely available through the Flair NLP framework (https://github.com/flairNLP/flair) under an MIT license and is compatible with all major operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

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