Abstract
Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Divoli, A., Attwood, T.K.: BioIE: extracting informative sentences from the biomedical literature. Bioinformatics 21(9), 2138–2139 (2005). Epub 2005 Feb 2
Cheng, D., Knox, C., Young, N., Stothard, P., Damaraju, S., Wishart, D.S.: PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res. 36(Web Server issue), W399–405 (2008). https://doi.org/10.1093//nar//gkn296
Rebholz-Schuhmann, D., Kirsch, H., Couto, F.: Facts from text–is text mining ready to deliver? PLoS Biol. 3(2), e65 (2005)
Yang, Z., Lin, H., Li, Y.: Exploiting the contextual cues for bio-entity name recognition in biomedical literature. J. Biomed. Inform. 41(4), 580–587 (2008). https://doi.org/10.1016//j.jbi.2008.01.002
Mitsumori, T., Fation, S., Murata, M., Doi, K., Doi, H.: Gene/protein name recognition based on support vector machine using dictionary as features. BMC Bioinform. 6(Suppl 1), S8 (2005)
Zhang, J., Shen, D., Zhou, G., Su, J., Tan, C.L.: Enhancing HMM-based biomedical named entity recognition by studying special phenomena. J. Biomed. Inform. 37(6), 411–422 (2004)
Settles, B.: ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics 21(14), 3191–3192 (2005)
Mika, S., Rost, B.: NLProt: extracting protein names and sequences from papers. Nucleic Acids Res. 32(Web Server issue), W634–637 (2004)
Gridach, M.: Character-level neural network for biomedical named entity recognition. J. Biomed. Inform. 70, 85–91 (2017). https://doi.org/10.1016//j.jbi.2017.05.002
Gao, B.T., Zhang, Y., Liu, B.: BioTrHMM: biomedical named entity recognition based on transfer learning. Comput. Appl. Res. 36(01), 51–54 (2019)
Lyu, C., Chen, B., Ren, Y., Ji, D.: Long short-term memory RNN for biomedical named entity recognition. BMC Bioinform. 18(1), 462 (2017). https://doi.org/10.1186//s12859-017-1868-5
Dang, T.H., Le, H.Q., Nguyen, T.M., Vu, S.T.: D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics 34(20), 3539–3546 (2018). https://doi.org/10.1093//bioinformatics//bty356
Kim, J.D., Ohta, T., Tateisi, Y., et al.: GENIA corpus–semantically annotated corpus for bio-textmining. Bioinformatics 19(suppl_1), i180 (2003)
Avery, I.T.: Conditional random field. Comput. Vis. 3(2), 637–640 (2012)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (2014)
Campos, V., Jou, B., Giro-I-Nieto, X., et al.: Skip RNN: learning to skip state updates in recurrent neural networks (2017)
Zeng, J., Che, J., Xing, C., et al.: A two-stage Bi-LSTM model for chinese company name recognition. In: International Conference on Artificial Intelligence: Methodology (2018)
Milakov, M., Gimelshein, N.: Online normalizer calculation for softmax (2018)
Anh, L.T., Arkhipov, M.Y., Burtsev, M.S.: Application of a hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition (2017)
Yang, X., Zhang, Z., Yang, R., et al.: Using deep learning to recognize biomedical entities. In: International Conference on Intelligent Systems & Knowledge Engineering. IEEE (2018)
Acknowledgment
This work is supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province in China (16KJD520003), National Natural Science Foundation of China (61502243, 61502247, 61572263), China Postdoctoral Science Foundation (2018M632349), Zhejiang Engineering Research Center of Intelligent Medicine under 2016E10011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lejun, G., Xiaolin, L., Xuemin, Y., Lipeng, Z., Yao, J., Ronggen, Y. (2019). CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-26969-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26968-5
Online ISBN: 978-3-030-26969-2
eBook Packages: Computer ScienceComputer Science (R0)