Semantic judgement of medical concepts : combining syntagmatic and paradigmatic information with the tensor encoding model
Symonds, Michael, Zuccon, Guido, Koopman, Bevan, Bruza, Peter D., & Nguyen, Anthony (2012) Semantic judgement of medical concepts : combining syntagmatic and paradigmatic information with the tensor encoding model. In Australasian Language Technology Association Workshop (ALTA 2012), 4-6 December 2012, University of Otago, Dunedin.
This paper outlines a novel approach for modelling semantic relationships within medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent research suggests that corpus-driven approaches are effective at automatically capturing semantic similarities between medical concepts, thus making them an attractive option for accessing semantic information.
Most previous corpus-driven methods only considered syntagmatic associations. In this paper, we adapt a recent approach that explicitly models both syntagmatic and paradigmatic associations. We show that the implicit similarity between certain medical concepts can only be modelled using paradigmatic associations. In addition, the inclusion of both types of associations overcomes the sensitivity to the training corpus experienced by previous approaches, making our method both more effective and more robust. This finding may have implications for researchers in the area of medical information retrieval.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Tensor Encoding Model, Medical Concept Similarity Judgement, Structural Linguistics, Semantic Similarity|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > OTHER INFORMATION AND COMPUTING SCIENCES (089900) > Information and Computing Sciences not elsewhere classified (089999)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 [please consult the author]|
|Deposited On:||13 Nov 2012 09:47|
|Last Modified:||26 Mar 2014 12:14|
Repository Staff Only: item control page