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Categorising Modality in Biomedical Texts

Thompson, P; Venturi, G; McNaught, J; Montemagni, S; Ananiadou, S

In: Proceedings of the LREC 2008 Workshop on Building and Evaluating Resources for Biomedical Text Mining; 2008. p. 27-34.

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Abstract

The accurate recognition of modal information is vital for the correct interpretation of statements. In this paper, we report on the collection a list of words and phrases that express modal information in biomedical texts, and propose a categorisation scheme according to the type of information conveyed. We have performed a small pilot study through the annotation of 202 MEDLINE abstracts according to our proposed scheme. Our initial results suggest that modality in biomedical statements can be predicted fairly reliably though the presence of particular lexical items, together with a small amount of contextual information.

Bibliographic metadata

Type of resource:
Content type:
Type of conference contribution:
Conference contribution title:
Publication date:
Proceedings start page:
27
Proceedings end page:
34
Proceedings pagination:
27-34
Contribution total pages:
8
Abstract:
The accurate recognition of modal information is vital for the correct interpretation of statements. In this paper, we report on the collection a list of words and phrases that express modal information in biomedical texts, and propose a categorisation scheme according to the type of information conveyed. We have performed a small pilot study through the annotation of 202 MEDLINE abstracts according to our proposed scheme. Our initial results suggest that modality in biomedical statements can be predicted fairly reliably though the presence of particular lexical items, together with a small amount of contextual information.

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:0j111
Created:
3rd November, 2009, 11:17:32
Last modified by:
Ananiadou, Sophia
Last modified:
6th April, 2011, 13:37:22

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