Extracting Drug-Drug interaction from text using negation features

  1. Bokharaeian, Behrouz
  2. Díaz Esteban, Alberto
  3. Ballesteros, Miguel
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2013

Número: 51

Páginas: 49-56

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

La extraccion de relaciones entre entidades es una tarea muy importante dentro del procesamiento de textos biomedicos. Se han desarrollado muchos algoritmos para este proposito aunque solo unos pocos han estudiado el tema de las interacciones entre farmacos. En este trabajo se ha estudiado el efecto de la negacion para esta tarea. En primer lugar, se describe como se ha extendido el corpus DrugDDI con anotaciones sobre negaciones y, en segundo lugar, se muestran una serie de experimentos en los que se muestra que tener en cuenta el efecto de la negacion puede mejorar la deteccion de interacciones entre farmacos cuando se combina con otros metodos de extraccion de relaciones.

Referencias bibliográficas

  • Amini, I., M. Sanderson, D. Martinez y X. Li. 2011. Search for clinical records: Rmit at trec 2011 medical track. En Proceedings of the 20th Text REtrieval Conference (TREC 2011).
  • Aronson, A. 2001. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. En Proceedings of the AMIA Symposium, páginas 17-27. URL http://metamap.nlm.nih.gov/.
  • Ballesteros, M., V. Francisco, A. Diaz, J. Herrera y P. Gervas. 2012. Inferring the scope of negation in biomedical documents. En Proceedings of the 13th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2012).
  • Bunescu, R., R. Ge, R. Kate, E. Marcotte, R. Mooney, A. Ramani y Y. Wong. 2005. Comparative experiments on learning information extractors for proteins and their interactions. Artificial Intelligence in Medicine, 33(2):139-155.
  • Bunescu R. y R. Mooney. 2005. A shortest path dependency kernel for relation extraction. En Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, páginas 724-731.
  • Cer, D., M. de Marneffe, D. Jurafsky y C. D. Manning. 2010. Parsing to Stanford Dependencies: Trade-offs between speed and accuracy. En Proceedings of the 7th International Conference on Language Resources and Evaluation(LREC 2010) .
  • Chapman, W., W. Bridewell, P. Hanbury, G. F. Cooper y B. G. Buchanan. 2001. A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of Biomedical Informactics, 34(5):301-310.
  • Chen, Y., F. Liu y B. Manderick. 2011. Extract Protein-Protein Interactions From the Literature Using Support Vector Machines with Feature Selection. Biomedical Engineering, Trends, Researchs and Technologies.
  • Frunza, O. y D. Inkpen. 2010. Extraction of disease-treatment semantic relations from biomedical sentences. Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, páginas 91-98.
  • Giuliano, C., A. Lavelli y L. Romano. 2005. Exploiting shallow linguistic information for relation extraction from biomedical literature. En Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics (EACL-2006), páginas 5-7.
  • Guodong, Z., Q. Longhua y F. Jianxi. 2010. Tree kernel-based semantic relation extraction with rich syntactic and semantic information. International Journal on Information Sciences, 180(8):1313-1325.
  • Krallinger, M., A. Valencia y L. Hirschman. 2008. Linking genes to literature: text mining, information extraction, and retrieval applications for biology. Genome Biology , 9(Suppl 2):S8.
  • Moschitti, A. 2006. Making Tree Kernels Practical for Natural Language Learning. En Proccedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics.
  • Nedellec, C. 2004. Machine Learning for Information Extraction in Genomics - State of the Art and Perspectives. Text Mining and its Applications, Springer Verlag.
  • Neuhaus, M. y H. Bunke. 2006. A Random Walk Kernel Derived from Graph Edit Distance. Lecture Notes in Computer Sci- ence, 4109(5):191-199.
  • Platt, J. 1998. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Advances in kernel methods - Support vector learning.
  • Pyysalo, S., A. Airola, J. Heimonen, J. Bjorne, F. Ginter y T. Salakoski. 2008. Comparative analysis of five proteinprotein interaction corpora. BMC bioinformatics, 9(Suppl 3):S6.
  • Segura-Bedmar, I., P. Martínez y D. Sánchez-Cisneros. 2011. En Proceedings of the 1st Challenge task on Drug-Drug Interaction Extraction (DDIExtraction 2011). CEUR Workshop Proceedings, Vol. 761.
  • Segura-Bedmar, I., P. Martínez y C. de Pablo Sánchez. 2011. Using a shallow linguistic kernel for drug-drug interaction extraction. Journal of Biomedical Informatics, 44(5):789-804.
  • Simões, G., D. Matos y H. Galhardas. 2013. A Labeled Graph Kernel for Relationship Extraction. CoRR, abs/1302.4874.
  • Stockley, I. H. 2007. Stockley's Drug Interaction. Pharmaceutical Press.
  • Thomas, P., M. Neves, I. Solt, D. Tikk y U. Leser. 2011. Relation extraction for drugdrug interactions using ensemble learning. En Proceedings of the First Challenge task on Drug-Drug Interaction Extraction (DDIExtraction 2011), pp:11-17.
  • Vincze, V., G. Szarvas, R. Farkas, G. Mora y J. Csirik. 2008. The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts. BMC Bioinformatics, 9(Suppl 11):S9.
  • Wishart, D. R., C. Knox , A. C. Guo, D. Cheng, S. Shrivastava, D. Tzur, B. Gautam, M. Hassanali. 2008. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 36(Suppl 1):D901-D906.
Fuente de los datos: Dialnet