Abstract
A hedge is a linguistic device used to avoid using a categorical sentence. Hedges can be used to determine whether a sentence is factual by merely regarding a sentence containing hedges as non-factual. In this paper, we perform a comparative experiment of various classification methods for hedge detection. Among four different classification methods, we observe that SVM shows the best performance and that the SVM-based method finally outperforms the best system in the CoNLL2010-ST task.
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References
Lakoff, G.: Hedges: a study in meaning criteria and the logic of fuzzy concepts. Chicago Linguistics Society Papers 8, 183–228 (1972)
Hyland, K.: Persuasion, interaction and the construction of knowledge: representing self and others in research writing. International Journal of English Studies 8(2), 8–18 (2008)
Light, M., Qiu, X.Y., Srinivasan, P.: The language of bioscience: facts, speculations, and statements in between. In: Proceedings of BioLINK 2004: Linking Biological Literature, Ontologies and Databases, pp. 17–24 (2004)
Medlock, B., Briscoe, T.: Weakly supervised learning for hedge classification in scientific literature. In: Proceedings of 45th Meeting of the Association for Computational Linguistics, pp. 992–999 (2007)
Szarvas, G.: Hedge classification in biomedical texts with a weakly supervised selection of keywords. In: Proceedings of 46th Meeting of the Association for Computational Linguistics, pp. 281–289 (2008)
Morante, R., Daelemans, W.: Learning the scope of hedge cues in biomedical texts. In: Proceedings of the BioNLP 2009 Workshop, pp. 28–36 (2009)
Farkas, R., Vincze, V., Mora, G., Csirik, J., Szarvas, G.: The CoNLL 2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text. In: Proceedings of the Shared Task, 14th Conference on Computational Natural Language Learning, Sweden, pp. 1–12 (2010)
Tang, B., Wang, X., Yuan, B., Fan, S.: A Cascade Method for Detecting Hedges and their Scope in Natural Language Text. In: Proceedings of the Shared Task, 14th Conference on Computational Natural Language Learning, Sweden, pp. 13–17 (2010)
Li, X., Shen, J., Gao, X., Wang, X.: Exploiting Rich Features for Detecting Hedges and Their Scope. In: Proceedings of the Shared Task, 14th Conference on Computational Natural Language Learning, Sweden, pp. 78–83 (2010)
Zhou, H., Li, X., Huang, D., Li, Z., Yang, Y.: Exploiting Multi-Features to Detect Hedges and Their Scope in Biomedical Texts. In: Proceedings of the Shared Task, 14th Conference on Computational Natural Language Learning, Sweden, pp. 106–113 (2010)
Sang, T.K., Veenstra, J.: Representing Text Chunks. In: Proc. of EACL 1999, pp. 173–179 (1999)
Tsuruoka, Y., Tateishi, Y., Kim, J.-D., Ohta, T., McNaught, J., Ananiadou, S., Tsujii, J.: Developing a Robust Part-of-Speech Tagger for Biomedical Text. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 382–392. Springer, Heidelberg (2005)
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Kang, SJ., Kang, IS., Na, SH. (2011). A Comparison of Classifiers for Detecting Hedges. In: Kim, Th., et al. U- and E-Service, Science and Technology. UNESST 2011. Communications in Computer and Information Science, vol 264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27210-3_32
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DOI: https://doi.org/10.1007/978-3-642-27210-3_32
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