生物医学命名实体识别（BioNER）研究进展

      最近把之前整理的一些生物医学命名实体识别（Biomedical Named Entity Recognition, BioNER）相关的论文做了一个BioNER Progress放在了github（https://github.com/lingluodlut/BioNER-Progress）上。主要内容包括BioNER进展中的代表论文列表，以及目前各个主要数据集上的一些先进结果和相关论文，希望对入门的同学有所帮助。

      论文列表首先给出一些综述论文，然后根据BioNER研究的发展历程依次给出了基于词典，基于规则和基于机器学习方法的代表性工作。机器学习的方法又细分为了基于传统机器学习模型（SVM、HMM、MEMM和CRF模型）以及现在主流的神经网络方法。

A paper list for BioNER

Over the past decades, many automatic BioNER methods have been proposed and used to recognise biomeidcal entities. They can be categorised into dictionary-based, rule-based and machine learning-based methods. Recently, neural network-based machine learning methods exhibit promising results.

1. Overview of BioCreative II gene mention recognition. Smith L, Tanabe L K, nee Ando R J, et al. Genome biology, 2008, 9(2): S2. [paper]
2. Biomedical named entity recognition: a survey of machine-learning tools. Campos D, Matos S, Oliveira J L. Theory and Applications for Advanced Text Mining, 2012: 175-195. [paper]
3. Chemical named entities recognition: a review on approaches and applications. Eltyeb S, Salim N. Journal of cheminformatics, 2014, 6(1): 17. [paper]
4. CHEMDNER: The drugs and chemical names extraction challenge. Krallinger M, Leitner F, Rabal O, et al. Journal of cheminformatics, 2015, 7(1): S1. [paper]
5. A comparative study for biomedical named entity recognition. Wang X, Yang C, Guan R. International Journal of Machine Learning and Cybernetics, 2015, 9(3): 373-382. [paper]

1. Using BLAST for identifying gene and protein names in journal articles. Krauthammer M, Rzhetsky A, Morozov P, et al. Gene, 2000, 259(1-2): 245-252. [paper]
2. Boosting precision and recall of dictionary-based protein name recognition. Tsuruoka Y, Tsujii J. Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine-Volume 13, 2003: 41-48. [paper]
3. Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature. Yang Z, Lin H, Li Y. Computational Biology and Chemistry, 2008, 32(4): 287-291. [paper]
4. A dictionary to identify small molecules and drugs in free text. Hettne K M, Stierum R H, Schuemie M J, et al. Bioinformatics, 2009, 25(22): 2983-2991. [paper] [dictionary]
5. LINNAEUS: a species name identification system for biomedical literature. Gerner M, Nenadic G, Bergman C M. BMC bioinformatics, 2010, 11(1): 85. [paper]

1. Toward information extraction: identifying protein names from biological papers. Fukuda K, Tsunoda T, Tamura A, et al. Pac symp biocomput. 1998, 707(18): 707-718. [paper]
2. A biological named entity recognizer. Narayanaswamy M, Ravikumar K E, Vijay-Shanker K. Biocomputing 2003. 2002: 427-438. [paper]
3. ProMiner: rule-based protein and gene entity recognition. Hanisch D, Fundel K, Mevissen H T, et al. BMC bioinformatics, 2005, 6(1): S14. [paper]
4. MutationFinder: a high-performance system for extracting point mutation mentions from text. Caporaso J G, Baumgartner Jr W A, Randolph D A, et al. Bioinformatics, 2007, 23(14): 1862-1865. [paper] [code]
5. Drug name recognition and classification in biomedical texts: a case study outlining approaches underpinning automated systems. Segura-Bedmar I, Martínez P, Segura-Bedmar M. Drug discovery today, 2008, 13(17-18): 816-823. [paper]
6. Investigation of unsupervised pattern learning techniques for bootstrap construction of a medical treatment lexicon. Xu R, Morgan A, Das A K, et al. Proceedings of the workshop on current trends in biomedical natural language processing, 2009: 63-70. [paper]
7. Linguistic approach for identification of medication names and related information in clinical narratives. Hamon T, Grabar N. Journal of the American Medical Informatics Association, 2010, 17(5): 549-554. [paper]
8. SETH detects and normalizes genetic variants in text. Thomas P, Rocktäschel T, Hakenberg J, et al. Bioinformatics, 2016, 32(18): 2883-2885. [paper] [code]
9. PENNER: Pattern-enhanced Nested Named Entity Recognition in Biomedical Literature. Wang X, Zhang Y, Li Q, et al. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018: 540-547. [paper]

SVM-based Methods

1. Tuning support vector machines for biomedical named entity recognition. Kazama J, Makino T, Ohta Y, et al. Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain-Volume 3, 2002: 1-8. [paper]
2. Biomedical named entity recognition using two-phase model based on SVMs. Lee K J, Hwang Y S, Kim S, et al. Journal of Biomedical Informatics, 2004, 37(6): 436-447. [paper]
3. Exploring deep knowledge resources in biomedical name recognition. GuoDong Z, Jian S. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications, 2004: 96-99. [paper]

HMM-based Methods

1. Named entity recognition in biomedical texts using an HMM model. Zhao S. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications, 2004: 84-87.[paper]
2. Annotation of chemical named entities. Corbett P, Batchelor C, Teufel S. Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, 2007: 57-64. [paper]
3. Conditional random fields vs. hidden markov models in a biomedical named entity recognition task. Ponomareva N, Rosso P, Pla F, et al. Proc. of Int. Conf. Recent Advances in Natural Language Processing, RANLP. 2007, 479: 483.[paper]

MEMM-based Mehtods

1. Cascaded classifiers for confidence-based chemical named entity recognition. Corbett P, Copestake A. BMC bioinformatics, 2008, 9(11): S4. [paper]
2. OSCAR4: a flexible architecture for chemical text-mining. Jessop D M, Adams S E, Willighagen E L, et al. Journal of cheminformatics, 2011, 3(1): 41. [paper]

CRF-based Methods

1. ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Settles B. Bioinformatics, 2005, 21(14): 3191-3192.[paper]
2. BANNER: an executable survey of advances in biomedical named entity recognition. Leaman R, Gonzalez G. Biocomputing 2008. 2008: 652-663.[paper]
3. Detection of IUPAC and IUPAC-like chemical names. Klinger R, Kolářik C, Fluck J, et al. Bioinformatics, 2008, 24(13): i268-i276. [paper]
4. Incorporating rich background knowledge for gene named entity classification and recognition. Li Y, Lin H, Yang Z. BMC bioinformatics, 2009, 10(1): 223. [paper]
5. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Jiang M, Chen Y, Liu M, et al. Journal of the American Medical Informatics Association, 2011, 18(5): 601-606. [paper]
6. ChemSpot: a hybrid system for chemical named entity recognition. Rocktäschel T, Weidlich M, Leser U. Bioinformatics, 2012, 28(12): 1633-1640. [paper]
7. Gimli: open source and high-performance biomedical name recognition. Campos D, Matos S, Oliveira J L. BMC bioinformatics, 2013, 14(1): 54. [paper]
8. tmVar: a text mining approach for extracting sequence variants in biomedical literature. Wei C H, Harris B R, Kao H Y, et al. Bioinformatics, 2013, 29(11): 1433-1439. [paper] [code]
9. Evaluating word representation features in biomedical named entity recognition tasks. Tang B, Cao H, Wang X, et al. BioMed research international, 2014, 2014. [paper]
10. Drug name recognition in biomedical texts: a machine-learning-based method. He L, Yang Z, Lin H, et al. Drug discovery today, 2014, 19(5): 610-617. [paper]
11. tmChem: a high performance approach for chemical named entity recognition and normalization. Leaman R, Wei C H, Lu Z. Journal of cheminformatics, 2015, 7(1): S3. [paper]
12. GNormPlus: an integrative approach for tagging genes, gene families, and protein domains. Wei C H, Kao H Y, Lu Z. BioMed research international, 2015, 2015. [paper]
13. Mining chemical patents with an ensemble of open systems[J]. Leaman R, Wei C H, Zou C, et al. Database, 2016, 2016. [paper]
14. nala: text mining natural language mutation mentions. Cejuela J M, Bojchevski A, Uhlig C, et al. Bioinformatics, 2017, 33(12): 1852-1858. [paper]

Neural Network-based Methods

1. Recurrent neural network models for disease name recognition using domain invariant features. Sahu S, Anand A. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016: 2216-2225. [paper]
2. Deep learning with word embeddings improves biomedical named entity recognition. Habibi M, Weber L, Neves M, et al. Bioinformatics, 2017, 33(14): i37-i48. [paper]
3. A neural joint model for entity and relation extraction from biomedical text. Li F, Zhang M, Fu G, et al. BMC bioinformatics, 2017, 18(1): 198. [paper]
4. A neural network multi-task learning approach to biomedical named entity recognition. Crichton G, Pyysalo S, Chiu B, et al. BMC bioinformatics, 2017, 18(1): 368. [paper] [code]
5. Disease named entity recognition from biomedical literature using a novel convolutional neural network. Zhao Z, Yang Z, Luo L, et al. BMC medical genomics, 2017, 10(5): 73. [paper]
6. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Luo L, Yang Z, Yang P, et al. Bioinformatics, 2018, 34(8): 1381-1388. [paper] [code]
7. GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text. Zhu Q, Li X, Conesa A, et al. Bioinformatics, 2018, 34(9): 1547-1554. [paper] [code]
8. D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Dang T H, Le H Q, Nguyen T M, et al. Bioinformatics, 2018, 34(20): 3539-3546. [paper] [code]
9. Transfer learning for biomedical named entity recognition with neural networks. Giorgi J M, Bader G D. Bioinformatics, 2018, 34(23): 4087-4094. [paper]
10. Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition. Wang Z, Qu Y, Chen L, et al. NAACL. 2018: 1-15. [paper]
11. Recognizing irregular entities in biomedical text via deep neural networks. Li F, Zhang M, Tian B, et al. Pattern Recognition Letters, 2018, 105: 105-113. [paper]
12. Cross-type biomedical named entity recognition with deep multi-task learning. Wang X, Zhang Y, Ren X, et al. Bioinformatics, 2019, 35(10): 1745-1752. [paper] [code]
13. Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings. Zhai Z, Nguyen D Q, Akhondi S, et al. Proceedings of the 18th BioNLP Workshop and Shared Task. 2019: 328-338. [paper] [code]
14. Chinese Clinical Named Entity Recognition Using Residual Dilated Convolutional Neural Network with Conditional Random Field. Qiu J, Zhou Y, Wang Q, et al. IEEE Transactions on NanoBioscience, 2019, 18(3): 306-315. [paper]
15. A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization. Zhao S, Liu T, Zhao S, et al. Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 817-824. [paper]
16. CollaboNet: collaboration of deep neural networks for biomedical named entity recognition. Yoon W, So C H, Lee J, et al. BMC bioinformatics, 2019, 20(10): 249. [paper] [code]
17. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Lee J, Yoon W, Kim S, et al. Bioinformatics, Advance article, 2019. [paper] [code]
18. HUNER: Improving Biomedical NER with Pretraining. Weber L, Münchmeyer J, Rocktäschel T, et al. Bioinformatics, Advance article, 2019. [paper] [code]

Others

1. TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Leaman R, Lu Z. Bioinformatics, 2016, 32(18): 2839-2846. [paper] [code]
2. A transition-based joint model for disease named entity recognition and normalization. Lou Y, Zhang Y, Qian T, et al. Bioinformatics, 2017, 33(15): 2363-2371. [paper] [code]

    此外，还总结给出了目前各个主要数据集上的一些先进结果。根据实体类型的不同分别为化学药物（Chemical）、疾病（Disease）、基因蛋白（Gene/Protein）、基因变异（Mutation）和物种（Species）的实体识别。

CHEMDNER

CHEMDNER (chemical compound and drug name recognition) task as part of the BioCreative IV challenge aims to promote the development of systems for the automatic recognition of chemical entities in text. It was divided into two tasks: one covered the indexing of documents with chemicals (chemical document indexing – CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition – CEM task). Here, we only focus on the CEM task.

The CHEMDNER corpus consists of 10,000 PubMed abstracts, which contains a total of 84,355 chemical entity mentions. The original corpus is divided into training set (3,500 abstracts), development set (3,500 abstracts) and test set (3,000 abstracts)

CDR-Chemical

CDR (chemical disease relation) task as part of the BioCreative V challenge aims to automatically extract CDRs from the literature. The CDR corpus consists of 1,500 PubMed abstracts with annotated chemicals, diseases and chemical-disease interactions, which contains a total of 15,933 chemical entity mentions. The original corpus is separated into training set (500 abstracts), development set (500 abstracts) and test set (500 abstracts).

NCBI-Disease

The NCBI Disease
corpus consists of 793 PubMed abstracts separated into training
(593), development (100) and test (100) subsets. It contains a total of 6,892
disease entity mentions.

CDR-Disease

CDR (chemical disease relation) task as part of the BioCreative V
challenge aims to automatically extract CDRs from the literature. The CDR
corpus consists of 1,500 PubMed abstracts with annotated chemicals, diseases
and chemical-disease interactions, which contains a total of 12,864 disease
entity mentions. The original corpus is separated into training set (500
abstracts), development set (500 abstracts) and test set (500 abstracts).

BC2GM

Gene Mention Tagging task as part of the BioCreative II
challenge is concerned with the named entity extraction of gene and gene
product mentions in text. The BC2GM corpus contains a total of 24,583 gene
entity mentions.

JNLPBA

JNLPBA corpus contains 2,404 abstracts extracted from MEDLINE using
the MeSH terms “human”, “blood- cell” and “transcription factor”. The manual annotation
of these abstracts was based on five classes of the GENIA ontology, namely
protein, DNA, RNA, cell line, and cell type. This corpus was used in the
Bio-Entity Recognition Task in BioNLP/NLPBA 2004, providing 2,000 abstracts for
training and the remaining 404 abstracts for testing. The overall results are
shown in the following table.

MuatationFinder corpus and tmVar
corpus

The MutationFinder corpus was established to guide the
construction of the patterns. The development data set is made up of 605 point
mutation mentions in 305 abstracts selected randomly from primary citations in
PDB. The evaluation data set is made up of 910 point mutation mentions in 508
abstracts annotated by two of the authors, not involved in the development of
the system.

The tmVar corpus comprises 500 abstracts manually annotated from which
334 were used for training tmVar while the remaining 166 were used for testing
it.

LINNAEUS corpus

The LINNAEUS corpus: A set of open access documents in text format, manually
annotated for species mention tags. It consists of 100 full-text documents from
the PMC OA document, which contains a total of 4,259 species entity mentions.