Abstract
The abstract of a scientific paper typically consists of sentences describing the background of study, its objective, experimental method and results, and conclusions. We discuss the task of identifying which of these “structural roles” each sentence in abstracts plays, with a particular focus on its application in building a literature retrieval system. By annotating sentences in an abstract collection with role labels, we can build a literature retrieval system in which users can specify the roles of the sentences in which query terms should be sought. We argue that this facility enables more goal-oriented search, and also makes it easier to narrow down search results when adding extra query terms does not work. To build such a system, two issues need to be addressed: (1) how we should determine the set of structural roles presented to users from which they can choose the target search area, and (2) how we should classify each sentence in abstracts by their structural roles, without relying too much on human supervision. We view the task of role identification as that of text classification based on supervised machine learning. Our approach is characterized by the use of structured abstracts for building training data. In structured abstracts, which is a format of abstracts popular in biomedical domains, sections are explicitly marked with headings indicating their structural roles, and hence they provide us with an inexpensive way to collect training data for sentence classifiers. Statistics on the structured abstracts contained in Medline give an insight on determining the set of sections to be presented to users as well.
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.
Similar content being viewed by others
References
Ad Hoc Working Group for Critical Appraisal of Medical Literature. A proposal for more informative abstracts of clinical articles 106(4), 598–604 (1987)
Charniak, E.: A maximum-entropy-inspired parser. In: Proceedings of the Second Meeting of North American Chapter of Association for Computational Linguistics (NAACL 2000), pp. 132–139 (2000)
Collins, M.: Head-Driven Statistical Models for Natural Language Processing. PhD dissertation, University of Pennsylvania (1999)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Halliday, M.A.K., Hasan, R.: Cohesion in English. Longman, London (1976)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 282–289. Morgan Kaufmann, San Francisco (2001)
Lawrence, S., Giles, C.L., Bollacker, K.: Digital libraries and autonomous citation indexing. IEEE Computer 32(6), 67–71 (1999)
Lee, L., Pereira, F.: Measures of distributional similarity. In: Proceedings of the 37th Annual Meeting of the Association for Comutational Linguistics (ACL 1999), pp. 25–32 (1999)
MEDLINE, U.S. National Library of Medicine (2002–2003) http://www.nlm.nih.gov/databases/databases_medline.html
Namazu (2000), http://www.namazu.org/
PubMed. U.S. National Library of Medicine (2003), http://www.ncbi.nlm.nih.gov/PubMed/
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of the Human Language Technology Conference North American Chapter of Association for Computational Linguistics (HLT NAACL 2003), Edmonton, Alberta, Canada, pp. 213–220. Association for Computational Linguistics (2003)
UIUC sentence splitter software, University of Illinois at Urbana-Champaign (2001), http://l2r.cs.uiuc.edu/~cogcomp/cc-software.htm
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shimbo, M., Yamasaki, T., Matsumoto, Y. (2005). Sentence Role Identification in Medline Abstracts: Training Classifier with Structured Abstracts. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds) Active Mining. Lecture Notes in Computer Science(), vol 3430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11423270_13
Download citation
DOI: https://doi.org/10.1007/11423270_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26157-5
Online ISBN: 978-3-540-31933-7
eBook Packages: Computer ScienceComputer Science (R0)