Abstract
In this paper we propose a novel model “recursive directed graph” based on feature structure, and apply it to represent the semantic relations of postpositive attributive structures in biomedical texts. The usages of postpositive attributive are complex and variable, especially three categories: present participle phrase, past participle phrase, and preposition phrase as postpositive attributive, which always bring the difficulties of automatic parsing. We summarize these categories and annotate the semantic information. Compared with dependency structure, feature structure, being recursive directed graph, enhances semantic information extraction in biomedical field. The annotation results show that recursive directed graph is more suitable to extract complex semantic relations for biomedical text mining.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Pyysalo S, Ginter F, Heimonen J, et al. BioInfer: A corpus for information extraction in the biomedical domain [J]. BMC Bioinformatics, 2007, 8(1): 50.
Kulick S, Bies A, Liberman M, et al. Integrated annotation for biomedical information extraction[C]//Proc of the Human Language Technology Conference and the Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL). Boston: Association for Computational Linguistics, 2004: 61–68.
Kim J D, Ohta T, Tsujii J. Corpus annotation for miningbiomedical events from literature [J]. BMC Bioinformatics, 2008, 9(1): 10.
Akane Y S, Yusuke M Y, Yuka Tateisi Y K, et al. Biomedical information extraction with predicate-argument structure patterns[C]//Proceedings of the First International Symposium on Semantic Mining in Biomedicine (SMBM). Budapest: CEUR, 2005.
Spasic I, Ananiadou S, McNaught J, et al. Text mining and ontologies in biomedicine: Making sense of raw text [J]. Briefings in Bioinformatics, 2005, 6(3): 239–251.
Cohen A M, Hersh W R. A survey of current work in biomedical text mining[J]. Briefings in Bioinformatics, 2005, 6(1): 57–71.
Zhang Y, Nivre J. Transition-based dependency parsing with rich non-local features[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2. Boston: Association for Computational Linguistics, 2011: 188–193.
Mel'čuk I. Dependency Syntax: Theory and Practice [M]. Herndon: SUNY Press, 1988.
Chen B, Wu H M, Lv C, et al. Semantic labeling of Chinese serial verb sentences based on feature structure [J]. Lecture Notes in Computer Science, 2013, 8229(1): 784–790.
Kenstowicz M, Kisseberth C. Generative Phonology [M]. New York: Academic Press, 1979.
Gazdar G. Generalized Phrase Structure Grammar [M]. Cambridge: Harvard University Press, 1985.
Dalrymple M. Lexical Functional Grammar [M]. New York: Academic Press, 2001.
Chen B, Ji D, Lv C. Building a Chinese semantic resource based on feature structure [J]. International Journal of Computer Processing of Languages, 2012, 24(1): 95–101.
Lu J, Lu K. Research on syntactic characteristics of computer English and its English to Chinese translation strategy[C]//Proc of 2013 Fifth International Conference on the Computational and Information Sciences (ICCIS). Los Alamitos: IEEE Computer Society, 2013: 1867–1870.
de Marneffe M C, MacCartney B, Manning C D. Ctenerating typed dependency parses from phrase structure parses[C]//Proceedings of LREC. Paris: EIRA, 2006: 449–454.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Supported by the National Natural Science Foundation of China (61202193, 61202304), the Major Projects of Chinese National Social Science Foundation (11&ZD189) and the Chinese Postdoctoral Science Foundation (2013M540593, 2014T70722)
Biography: CHEN Bo, female, Ph.D., Associate professor, research direction: natural language processing.
Rights and permissions
About this article
Cite this article
Chen, B., Lü, C., Wei, X. et al. Semantic relation annotation for biomedical text mining based on recursive directed graph. Wuhan Univ. J. Nat. Sci. 20, 141–145 (2015). https://doi.org/10.1007/s11859-015-1072-2
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11859-015-1072-2