Abstract
Negation and speculation are common in natural language text. Many applications, such as biomedical text mining and clinical information extraction, seek to distinguish positive/factual objects from negative/speculative ones (i.e., to determine what is negated or speculated) in biomedical texts. This paper proposes a novel task, called negation and speculation target identification, to identify the target of a negative or speculative expression. For this purpose, a new layer of the target information is incorporated over the BioScope corpus and a machine learning algorithm is proposed to automatically identify this new information. Evaluation justifies the effectiveness of our proposed approach on negation and speculation target identification in biomedical texts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carreras, X., Mà rquez, L.: Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling. In: Proceedings of the 9th Conference on Computational Natural Language Learning, pp. 152–164 (2005)
Conger, A.J.: Integration and generalization of kappas for multiple raters. Psychological Bulletin 88(2), 322–328 (1980)
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: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, Uppsala, Sweden, pp. 1–12 (2010)
Jiang, Z., Ng, H.T.: Semantic Role Labeling of NomBank: A Maximum Entropy Approach. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 138–145 (2006)
Light, M., Qiu, X., Srinivasan, P.: The Language of Bioscience: Facts, Peculations, and Statements in Between. In: Proceedings of the HLT BioLINK 2004, pp. 17–24 (2004)
Medlock, B., Briscoe, T.: Weakly supervised learning for hedge classification in scientific literature. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, pp. 992–999 (2007)
Morante, R., Liekens, A., Daelemans, W.: Learning the scope of negation in biomedical texts. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Honolulu, HI, pp. 715–724 (2008)
Morante, R., Sporleder, C. (eds.): Proceedings of the Workshop on Negation and Speculation in Natural Language Processing. University of Antwerp, Uppsala (2010)
Morante, R., Sporleder, C.: Modality and Negation: An Introduction to the Special Issue. Computational Linguistics 38(2), 223–260 (2012)
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis: Foundations and Trends. Information Retrieval 2(12), 1–135 (2008)
Petrov, S., Klein, D.: Improved Inference for Unlexicalized Parsing. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, pp. 404–411 (2007)
Szarvas, G.: Hedge classification in biomedical texts with a weakly supervised selection of keywords. In: Proceedings of the 46th Annual Meeting of the Association of Computational Linguistics, pp. 281–289 (2008)
Tateisi, Y., Yakushiji, A., Ohta, T., Tsujii, J.: Syntax Annotation for the GENIA Corpus. In: Proceedings of IJCNLP, Companion Volume, pp. 222–227 (2005)
Vincze, V., Szarvas, G., Farkas, R., Mora, G., Csirik, J.: The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics 9(suppl. 11), S9+ (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zou, B., Zhou, G., Zhu, Q. (2014). Negation and Speculation Target Identification. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-45924-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45923-2
Online ISBN: 978-3-662-45924-9
eBook Packages: Computer ScienceComputer Science (R0)