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Improving text mining with controlled natural language: a case study for protein interactions


Kuhn, T; Royer, L; Fuchs, N E; Schroeder, M (2006). Improving text mining with controlled natural language: a case study for protein interactions. In: 3rd International Workshop on Data Integration in the Life Sciences (DILS 2006), Hinxton, UK, 20 July 2006 - 22 July 2006, 66-81.

Abstract

Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.

Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.

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7 citations in Web of Science®
12 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:22 July 2006
Deposited On:25 Jul 2008 08:40
Last Modified:05 Apr 2016 12:24
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:4075
ISSN:0302-9743 (P) 1611-3349 (E)
ISBN:978-3-540-36593-8
Publisher DOI:10.1007/11799511_7
Related URLs:http://opac.nebis.ch/F/?local_base=NEBIS&con_lng=GER&func=find-b&find_code=SYS&request=005219863
Permanent URL: http://doi.org/10.5167/uzh-2781

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