nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

A CRFs-Based Approach Empowered with Word Representation Features to Learning Biomedical Named Entities from Medical Text

verfasst von : Wenxiu Xie, Sihui Fu, Shengyi Jiang, Tianyong Hao

Erschienen in: Emerging Technologies for Education

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Targeting at identifying specific types of entities, biomedical named entity recognition is a fundamental task of biomedical text processing. This paper presents a CRFs-based approach to learning disease entities by identifying their boundaries in texts. Two types of word representation features are proposed and used including word embedding features and cluster-based features. In addition, an external disease dictionary feature is also explored in the learning process. Based on a publically available NCBI disease corpus, we evaluate the performance of the CRFs-based model with the combination of these word representation features. The results show that using these features can significantly improve BNER performance with an increase of 24.7% on F1 measure, demonstrating the effectiveness of the proposed features and the feature-empowered approach.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Discovering the Recent Research in Natural Language Processing Field Based on a Statistical Approach

Nächstes Kapitel Computer-Assisted Content Analysis in Risk Identification of Public-Private Partnership Projects

http://taku910.github.io/crfpp/.

https://www.ncbi.nlm.nih.gov/pubmed/.

http://www.nactem.ac.uk/GENIA/tagger/.

https://nlp.stanford.edu/projects/glove/.

https://github.com/percyliang/brown-cluster/.

https://www.pharmgkb.org/downloads/.

For 2013 NIH corpus, it takes about 19 h to cluster words into 500 clusters, but 89 h into 1000 clusters on a machine with i5 CPU and 8 GB memory.

Kazama, J., Makino, T., Ohta, Y., Tsujii, J.: Tuning support vector machines for biomedical named entity recognition. In: Proceedings of the ACL Workshop on Natural Language Processing in the Biomedical Domain, vol. 3, pp. 1–8 (2002)

Athenikos, S.J., Han, H.: Biomedical question answering: A survey. Comput. Methods Prog. Biomed. 99, 1–24 (2010)CrossRef

Leaman, R., Gonzalez, G.: BANNER: an executable survey of advances in biomedical named entity recognition. In: Pacific Symposium on Biocomputing, vol. 13, pp. 652–663 (2008)

Yao, L., Liu, H., Liu, Y., Li, X., Anwar, M.W.: Biomedical named entity recognition based on deep neural network. Int. J. Hybrid Inf. Technol. 8(8), 279–288 (2015)CrossRef

Tang, B., Cao, H., Wang, X., Chen, Q., Xu, H.: Evaluating word representation features in biomedical named entity recognition tasks. Biomed. Res. Int. 1–6 (2014)

Wang, X., Yang, C., Guan, R.: A comparative study for biomedical named entity recognition. Int. J. Mach. Learn.Cybern., 1–10 (2015)

Li, K., Ai, W., Tang, Z., Zhang, F., Jiang, L., Li, K., Hwang, K.: Hadoop recognition of biomedical named entity using conditional random fields. IEEE Trans. Parallel Distrib. Syst. 26(11), 3040–3051 (2015)CrossRef

Fries, J., Wu, S., Ratner, A., Ré, C.: SwellShark: a generative model for biomedical named entity recognition without labeled data (2017). arXiv preprint arXiv:1704.06360

Zhang, S., Elhadad, N.: Unsupervised biomedical named entity recognition: experiments with clinical and biological texts. J. Biomed. Inf. 46(6), 1088–1098 (2013)CrossRef

10.

Kuksa, P.P., Qi, Y.: Semi-supervised bio-named entity recognition with word-codebook learning. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 25–36. Society for Industrial and Applied Mathematics (2010)

11.

Munkhdalai, T., Li, M., Yun, U., Namsrai, O.E., Ryu, K.H.: An active co-training algorithm for biomedical named-entity recognition. JIPS 8(4), 575–588 (2012)

12.

Munkhdalai, T., Li, M., Batsuren, K., Park, H.A., Choi, N.H., Ryu, K.H.: Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations. J. Cheminf. 7(1), 1–8 (2015)CrossRef

13.

Gridach, M.: Character-level neural network for biomedical named entity recognition. J. Biomed. Inf. 70, 85–91 (2017)CrossRef

14.

Vlachos, A.: Tackling the BioCreative2 gene mention task with conditional random fields and syntactic parsing. In: Proceedings of the Second BioCreative Challenge Workshop, pp. 85–87 (2007)

15.

Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on EMNLP, pp. 1532–1543 (2014)

16.

John, V.: A survey of neural network techniques for feature extraction from text (2017). http://arxiv.org/abs/1704.08531

17.

Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, vol. 8, pp. 282–289 (2001)

18.

Jain, D.: Supervised named entity recognition for clinical data. CLEF 2015 Online Working Notes (2015)

19.

Wang, S.K., Li, S., Chen, T.: Recognition of Chinese medicine named entity based on condition random field. J. Xiamen Univ. 48(3), 359–364 (2009)

20.

Zweig, G., Nguyen, P., Van Compernolle, D., et al.: Speech recognition with segmental conditional random fields: a summary of the JHU CLSP 2010 summer workshop. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5044–5047 (2011)

21.

Wallach, H.M.: Conditional random fields: an introduction. University of Pennsylvania (2004)

22.

Doğan, R.I., Leaman, R., Lu, Z.: NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Inf. 47, 1–10 (2014)CrossRef

23.

Zhao, H., Huang, C.-N., Li, M.: An improved chinese word segmentation system with conditional random field. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pp. 162–165 (2006)

Titel: A CRFs-Based Approach Empowered with Word Representation Features to Learning Biomedical Named Entities from Medical Text
verfasst von: Wenxiu Xie
Sihui Fu
Shengyi Jiang
Tianyong Hao
Verlag: Springer International Publishing
Buch: Emerging Technologies for Education
Print ISBN: 978-3-319-71083-9

Electronic ISBN: 978-3-319-71084-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71084-6_61

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner