Using typed dependencies to study and recognise conceptualisation zones in biomedical literature

Groza, Tudor (2013) Using typed dependencies to study and recognise conceptualisation zones in biomedical literature. PLoS One, 8 11: e79570.1-e79570.14. doi:10.1371/journal.pone.0079570

Author Groza, Tudor
Title Using typed dependencies to study and recognise conceptualisation zones in biomedical literature
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-11-18
Sub-type Article (original research)
DOI 10.1371/journal.pone.0079570
Open Access Status DOI
Volume 8
Issue 11
Start page e79570.1
End page e79570.14
Total pages 14
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Subject 1300 Biochemistry, Genetics and Molecular Biology
1100 Agricultural and Biological Sciences
Abstract In the biomedical domain, authors publish their experiments and findings using a quasi-standard coarse-grained discourse structure, which starts with an introduction that sets up the motivation, continues with a description of the materials and methods, and concludes with results and discussions. Over the course of the years, there has been a fair amount of research done in the area of scientific discourse analysis, with a focus on performing automatic recognition of scientific artefacts/conceptualisation zones from the raw content of scientific publications. Most of the existing approaches use Machine Learning techniques to perform classification based on features that rely on the shallow structure of the sentence tokens, or sentences as a whole, in addition to corpus-driven statistics. In this article, we investigate the role carried by the deep (dependency) structure of the sentences in describing their rhetorical nature. Using association rule mining techniques, we study the presence of dependency structure patterns in the context of a given rhetorical type, the use of these patterns in exploring differences in structure between the rhetorical types, and their ability to discriminate between the different rhetorical types. Our final goal is to provide a series of insights that can be used to complement existing classification approaches. Experimental results show that, in particular in the context of a fine-grained multi-class classification context, the association rules emerged from the dependency structure are not able to produce uniform classification results. However, they can be used to derive discriminative pair-wise classification mechanisms, in particular for some of the most ambiguous types.
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID DE120100508
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
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Created: Tue, 17 Dec 2013, 07:14:33 EST by Dr Tudor Groza on behalf of School of Information Technol and Elec Engineering