Shallow Features for Differentiating Disease-Treatment Relations Using Supervised Learning A Pilot Study

  • Dimitrios Kokkinakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5729)


Clinical narratives provide an information rich, nearly unexplored corpus of evidential knowledge that is considered as a challenge for practitioners in the language technology field, particularly because of the nature of the texts (excessive use of terminology, abbreviations, orthographic term variation), the significant opportunities for clinical research that such material can provide and the potentially broad impact that clinical findings may have in every day life. It is therefore recognized that the capability to automatically extract key concepts and their relationships from such data will allow systems to properly understand the content and knowledge embedded in the free text which can be of great value for applications such as information extraction and question & answering. This paper gives a brief presentation of such textual data and its semantic annotation, and discusses the set of semantic relations that can be observed between diseases and treatments in the sample. The problem is then designed as a supervised machine learning task in which the relations are tried to be learned using pre-annotated data. The challenges designing the problem and empirical results are presented.


Support Vector Machine Semantic Relation Semantic Relationship Relation Extraction Sequential Minimal Optimization 
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.


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  1. 1.
    de Bruijn, B., Martin, J.: Literature mining in molecular biology. In: Baud, R., Ruch, P. (eds.) EFMI Workshop on NLP in Biomedical Applications, Nicosia, Cyprus, pp. 1–5 (2002)Google Scholar
  2. 2.
    Girju, R., Nakov, P., Nastase, V., Szpakowicz, S., Turney, P., Yuret, D.: SemEval-2007 Task 04: Classification of Semantic Relations between Nominals (2007)Google Scholar
  3. 3.
    Rosario, B., Hearst, M.A.: Classifying Semantic relations in Bioscience Texts. In: Proceedings of the 42nd Annual Meeting on ACL, Barcelona (2004)Google Scholar
  4. 4.
    Vintar, S., Buitelaar, P., Volk, M.: Semantic relations in concept-based cross-language medical information retrieval. In: Adaptive Text Extraction&Mining Workshop, Croatia (2003)Google Scholar
  5. 5.
    Roberts, A., Gaizauskas, R., Hepple, M.: Extracting Clinical Relationships from Patient Narratives. In: BioNLP 2008, Ohio, USA, pp. 10–18 (2008)Google Scholar
  6. 6.
    Zhou, G., Su, J., Zhang, J., Zhang, M.: Exploring Various Knowledge in Relation Extraction. In: Proc. of the 43rd Annual Meeting of the ACL, Michigan, pp. 427–434 (2005)Google Scholar
  7. 7.
    Sibanda, T.C.: Was the Patient Cured? Understanding Semantic Categories and Their Relationships in Patient Records. Master Thesis. Electrical Engineering & CS. MIT (2006)Google Scholar
  8. 8.
    Kokkinakis, D., Thurin, A.: Applying MeSH® to the (Swedish) Clinical Domain - Evaluation and Lessons learned. 6th Scand. Health Info. Conf. Kalmar, Sweden (2008)Google Scholar
  9. 9.
    Kokkinakis, D.: Reducing the Effect of Name Explosion. LREC Workshop: Beyond Named Entity Recognition Semantic Labeling for NLP tasks. Portugal (2004)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  11. 11.
    Girju, R.: Support vector machines applied to the classification of semantic relations in nominalized noun phrases. In: HLT-NAACL W’hop on Lexical Semantics, Boston. US (2004)Google Scholar
  12. 12.
    Wang, T., Li, Y., Bontcheva, K., Cunningham, H., Wang, J.: Automatic extraction of hierarchical relations from text. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 215–229. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Giles, C.B., Wren, J.D.: Large-scale directional relationship extraction and resolution. BMC Bioinformatics 9(Suppl. 9), S11 (2008)CrossRefGoogle Scholar
  14. 14.
    Pustejovsky, J., Castaňo, J., Zhang, J.: Robust Relational Parsing over Biomedical literature: Extracting Inhibit Relations. In: Proc. 7th Biocomputing Symposium (2002)Google Scholar
  15. 15.
    Mustafaraj, E., Hoof, M., Freisleben, B.: Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles. In: Kao, A., Poteet, S. (eds.) NLP&TM, pp. 46–67. Springer, Heidelberg (2007)Google Scholar
  16. 16.
    Sætre, R., Sagae, K., Tsujii, J.: Syntactic features for protein-protein interaction extraction. In: 2nd International Symposium on Languages in Biology and Medicine, Singapore (2007)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dimitrios Kokkinakis
    • 1
  1. 1.Department of Swedish LanguageSpråkdata, University of GothenburgGothenburgSweden

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