Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection

  • Mathias Verbeke
  • Paolo Frasconi
  • Vincent Van Asch
  • Roser Morante
  • Walter Daelemans
  • Luc De Raedt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)


Hedge cue detection is a Natural Language Processing (NLP) task that consists of determining whether sentences contain hedges. These linguistic devices indicate that authors do not or cannot back up their opinions or statements with facts. This binary classification problem, i.e. distinguishing factual versus uncertain sentences, only recently received attention in the NLP community. We use kLog, a new logical and relational language for kernel-based learning, to tackle this problem. We present results on the CoNLL 2010 benchmark dataset that consists of a set of paragraphs from Wikipedia, one of the domains in which uncertainty detection has become important. Our approach shows competitive results compared to state-of-the-art systems.


statistical relational learning kernel methods natural language learning 


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  1. 1.
    Lakoff, G.: Hedges: A study in meaning criteria and the logic of fuzzy concepts. Journal of Philosophical Logic 2 (1973)Google Scholar
  2. 2.
    Hyland, K.: Hedging in scientific research articles, Amsterdam (1998)Google Scholar
  3. 3.
    Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proc. of CoNLL 2003, Edmonton (2003)Google Scholar
  4. 4.
    Medlock, B., Briscoe, T.: Weakly supervised learning for hedge classification in scientific literature. In: Proc. of ACL 2007, Prague (2007)Google Scholar
  5. 5.
    Szarvas, G.: Hedge classification in biomedical texts with a weakly supervised selection of keywords. In: Proc. of ACL 2008, Ohio (2008)Google Scholar
  6. 6.
    Light, M., Qiu, X., Srinivasan, P.: The language of bioscience: facts, speculations, and statements in between. In: Proc. of HLT-NAACL 2004 – BioLINK (2004)Google Scholar
  7. 7.
    Medlock, B.: Exploring hedge identification in biomedical literature. Journal of Biomedical Informatics 41 (2008)Google Scholar
  8. 8.
    Frasconi, P., Costa F., De Raedt L., De Grave K.: kLog - a language for logical and relational learning with kernels, Technical Report (2011),
  9. 9.
    Kim, J., Ohta, T., Pyysalo, S., Kano, Y., Tsujii, J.: Overview of BioNLP’09 shared task on event extraction. In: Proc. of the Workshop on Current Trends in Biomedical NLP – Shared Task, Colorado (2009)Google Scholar
  10. 10.
    Farkas, R., Vincze, V., Móra, G., Csirik, J., Szarvas, G.: The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text. In: Proc. of CoNLL 2010 – Shared Task, Uppsala (2010)Google Scholar
  11. 11.
    Chang, C.-C., Lin C.-J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  12. 12.
    Morante, R., Van Asch, V., Daelemans, W.: Memory-based resolution of in-sentence scopes of hedge cues. In: Proc. of CoNLL 2010 – Shared Task, Uppsala (2010)Google Scholar
  13. 13.
    Daelemans, W., van den Bosch, A.: Memory-based language processing. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  14. 14.
    Kilicoglu, H., Bergler, S.: Recognizing speculative language in biomedical research articles: a linguistically motivated perspective. BMC Bioinformatics (2008)Google Scholar
  15. 15.
    Ganter, V., Strube, M.: Finding hedges by chasing weasels: Hedge detection using Wikipedia tags and shallow linguistic features. In: Proc. of ACL-IJCNLP 2009 Conference Short Papers, Suntec (2009)Google Scholar
  16. 16.
    Nivre, J.: Inductive Dependency Parsing. In: Text, Speech and Language Technology. Springer (2006)Google Scholar
  17. 17.
    Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems: The Complete Book. Prentice Hall Press (2008)Google Scholar
  18. 18.
    Vincze, V., Szarvas, G., Farkas, R., Móra, G., Csirik, J.: The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics (2008)Google Scholar
  19. 19.
    Velldal, E.: Detecting Uncertainty in Biomedical Literature: A Simple Disambiguation Approach Using Sparse Random Indexing. In: Proc. of the Fourth International Symposium on Semantic Mining in Biomedicine (SMBM), Cambridgeshire (2010)Google Scholar
  20. 20.
    Costa, F., De Grave, K.: Fast neighborhood subgraph pairwise distance kernel. In: Proc. of the 26th International Conference on Machine Learning, Haifa (2010)Google Scholar
  21. 21.
    Buchholz, S., Marsi, E.: CoNLL-X shared task on multilingual dependency parsing. In: Proc. of the Tenth Conference on Computational Natural Language Learning (CoNLL-X 2006), New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mathias Verbeke
    • 1
  • Paolo Frasconi
    • 2
  • Vincent Van Asch
    • 3
  • Roser Morante
    • 3
  • Walter Daelemans
    • 3
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenBelgium
  2. 2.Dipartimento di Sistemi e InformaticaUniversità degli Studi di FirenzeItaly
  3. 3.Department of LinguisticsUniversiteit AntwerpenBelgium

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