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Machine Reading for Extraction of Bacteria and Habitat Taxonomies

  • Parisa KordjamshidiEmail author
  • Wouter Massa
  • Thomas Provoost
  • Marie-Francine Moens
Conference paper
  • 622 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 574)

Abstract

There is a vast amount of scientific literature available from various resources such as the internet. Automating the extraction of knowledge from these resources is very helpful for biologists to easily access this information. This paper presents a system to extract the bacteria and their habitats, as well as the relations between them. We investigate to what extent current techniques are suited for this task and test a variety of models in this regard. We detect entities in a biological text and map the habitats into a given taxonomy. Our model uses a linear chain Conditional Random Field (CRF). For the prediction of relations between the entities, a model based on logistic regression is built. Designing a system upon these techniques, we explore several improvements for both the generation and selection of good candidates. One contribution to this lies in the extended flexibility of our ontology mapper that uses an advanced boundary detection and assigns the taxonomy elements to the detected habitats. Furthermore, we discover value in the combination of several distinct candidate generation rules. Using these techniques, we show results that are significantly improving upon the state of art for the BioNLP Bacteria Biotopes task.

Keywords

Conditional Random Field Relation Extraction Coreference Resolution Conditional Random Field Model Stanford Parser 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research is supported by grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov) and by Research Foundation Flanders (FWO) (grant G.0356.12). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Also we would like to thank the reviewers for their insightful comments and remarks.

References

  1. 1.
    Bossy, R., Golik, W., Ratkovic, Z., Bessières, P., Nédellec, C.: BioNLP shared task 2013 - an overview of the bacteria biotope task. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 161–169 (2013)Google Scholar
  2. 2.
    Nédellec, C., Bossy, R., Kim, J.D., Kim, J.J., Ohta, T., Pyysalo, S., Zweigenbaum, P.: Overview of BioNLP shared task 2013. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 1–7 (2013)Google Scholar
  3. 3.
    Bossy, R., Jourde, J., Bessieres, P., van de Guchte, M., Nedellec, C.: BioNLP shared task 2011 - bacteria biotope. In: Proceedings of BioNLP Shared Task 2011 Workshop. ACL, pp. 56–64 (2011)Google Scholar
  4. 4.
    Bjorne, J., Salakoski, T.: Generalizing biomedical event extraction. In: Proceedings of BioNLP Shared Task 2011 Workshop. ACL (2011)Google Scholar
  5. 5.
    Nguyen, N.T.H., Tsuruoka, Y.: Extracting bacteria biotopes with semi-supervised named entity recognition and coreference resolution. In: Proceedings of BioNLP Shared Task 2011 Workshop. ACL (2011)Google Scholar
  6. 6.
    Ratkovic, Z., Golik, W., Warnier, P., Veber, P., Nedellec, C.: Task bacteria biotope-the Alvis system. In: Proceedings of BioNLP Shared Task 2011 Workshop. ACL (2011)Google Scholar
  7. 7.
    Bannour, S., Audibert, L., Soldano, H.: Ontology-based semantic annotation: an automatic hybrid rule-based method. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 139–143 (2013)Google Scholar
  8. 8.
    Claveau, V.: IRISA participation to BioNLP-ST 2013: lazy-learning and information retrieval for information extraction tasks. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 188–196 (2013)Google Scholar
  9. 9.
    Grouin, C.: Building a contrasting taxa extractor for relation identification from assertions: biological taxonomy & ontology phrase extraction system. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 144–152 (2013)Google Scholar
  10. 10.
    Karadeniz, I., Özgür, A.: Bacteria biotope detection, ontology-based normalization, and relation extraction using syntactic rules. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 170–177 (2013)Google Scholar
  11. 11.
    Björne, J., Salakoski, T.: TEES 2.1: automated annotation scheme learning in the BioNLP 2013 shared task. In: Proceedings of the BioNLP Shared Task 2013 Workshop, Sofia, Bulgaria. ACL, pp. 16–25 (2013)Google Scholar
  12. 12.
    Klein, D., Manning, C.D.: Fast exact inference with a factored model for natural language parsing. In: Advances in Neural Information Processing Systems (NIPS), vol. 15, pp. 3–10. MIT Press (2003)Google Scholar
  13. 13.
    Sutton, C., McCallum, A.: An Introduction to conditional random fields for relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press (2007)Google Scholar
  14. 14.
    Lei, J., Tang, B., Lu, X., Gao, K., Jiang, M., Xu, H.: A comprehensive study of named entity recognition in chinese clinical text. J. Am. Med. Inform. Assoc. 21, 808–814 (2014)CrossRefGoogle Scholar
  15. 15.
    McCallum, A., Schultz, K., Singh, S.: FACTORIE: probabilistic programming via imperatively defined factor graphs. In: Neural Information Processing Systems (NIPS) (2009)Google Scholar
  16. 16.
    Porter, M.: An algorithm for suffix stripping. Program 14, 130–137 (1980)CrossRefGoogle Scholar
  17. 17.
    Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Proceedings of the 3rd ACL Workshop on Very Large Corpora, Cambridge, MA, USA, pp. 82–94 (1995)Google Scholar
  18. 18.
    Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)MathSciNetGoogle Scholar
  19. 19.
    Kordjamshidi, P., Moens, M.F.: Global machine learning for spatial ontology population. J. Web Semant. 30, 3–21 (2015)CrossRefGoogle Scholar
  20. 20.
    Kordjamshidi, P., Moens, M.F.: Designing constructive machine learning models based on generalized linear learning techniques. In: NIPS Workshop on Constructive Machine Learning (2013)Google Scholar
  21. 21.
    Kordjamshidi, P., Roth, D., Moens, M.F.: Structured learning for spatial information extraction from biomedical text: bacteria biotopes. BMC Bioinform. 16, 129 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Parisa Kordjamshidi
    • 1
    • 2
    Email author
  • Wouter Massa
    • 2
  • Thomas Provoost
    • 2
  • Marie-Francine Moens
    • 2
  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Department of Computer ScienceKU LeuvenHeverleeBelgium

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