[Elsnet-list] Detection and classification of anatomical entities in text - new tools, resources and paper

Paul Thompson Paul.Thompson at manchester.ac.uk
Fri Dec 16 13:23:59 CET 2011


Anatomical entities are central to much of biomedical discourse and must be considered in any attempt to fully analyse biomedical scientific text. However, while a wealth of tools and resources have been introduced in domain natural language processing efforts for the recognition of molecular level entity (gene, protein, chemical) and organism name mentions in text, there has been little study of the recognition of mentions of anatomical entities such as tissues and organs.

Effects involving anatomical entities such as tissues and organs are key to understanding the connections between molecular-level events and organism-level effects such as disease. For example, to analyse a statement like "NO synhase may induce an anti-tumour effect by affecting blood vessel growth",  it is necessary to recognize not only the molecular entity (NO synhase) and the affected pathological entity (tumour), but also the anatomical entity (blood vessel) as well as the statements of their relationships.

The National Centre for Text Mining (NaCTeM) at the University of Manchester have developed a new set of tools and lexical resources that make use of the wealth of anatomy domain ontologies available in the OBO Foundry collection of Open Biological and Biomedical Ontologies (http://www.obofoundry.org/) to facilitate anatomical entity mention detection and classification.

The following resources have been made available for academic use:

* Approximate string-matching ontology lookup tool
* Selection of OBO foundry ontologies relevant to physical anatomical entity mention recognition
* Set of 5000 common noun phrases from PubMed annotated to identify anatomical entity mentions
* Lexical items relevant to anatomical entities together with their upper-level ontological categories in the various OBO anatomy resources

A newly published paper provides further details:

Sampo Pyysalo, Tomoko Ohta, Jun'ichi Tsujii and Sophia Ananiadou. (2011). Anatomical Entity Recognition with Open Biomedical Ontologies. In Proceedings of the Fourth International Symposium on Languages in Biology and Medicine (LBM 2011). (http://www.nactem.ac.uk/papers/Pyysalo_2011_Anatomical.pdf)

For further details and to download the resources, please refer to the anatomy resources page on the NaCTeM site (http://www.nactem.ac.uk/resources.php?view=9)


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Paul Thompson
Research Associate
School of Computer Science
National Centre for Text Mining
Manchester Interdisciplinary Biocentre
University of Manchester
131 Princess Street
Manchester
M1 7DN
UK
Tel: 0161 306 3091
http://personalpages.manchester.ac.uk/staff/Paul.Thompson/


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