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Automatic Biological Term Annotation

Using n-gram and Classification Models

Language EnglishEnglish
Book Paperback
Book Automatic Biological Term Annotation Sittichai Jiampojamarn
Libristo code: 06819675
Publishers VDM Verlag Dr. Müller, November 2008
Exciting research in biology has resulted in a large§amount of biological publications. §Knowledge d... Full description
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Exciting research in biology has resulted in a large§amount of biological publications. §Knowledge discovery in biology becomes an interesting§task which can be established§by recognizing terms in text to extract useful§information such as interaction relationships.§§We propose the Automatic Biological Term Annotation§(ABTA) system which uses classification methods to§annotate terms in text. A novel method is presented§to express lexical features in pattern notations.§Prefix and suffix characters are used instead of§lists of potential terms or external resources. We§demonstrate that part-of-speech tag information is§the most effective attribute. Creating classification§exemplars is conducted from text by using word n-gram§model. We illustrate improvements on our system's§performance which depends on the feature attributes§we define. Biological concept markers are also§assigned to each located term indicating its meaning.§Our results are comparable to the performance of§other existing systems while our system retains§simplicity and generalizability. Exciting research in biology has resulted in a large§amount of biological publications. §Knowledge discovery in biology becomes an interesting§task which can be established§by recognizing terms in text to extract useful§information such as interaction relationships.§We propose the Automatic Biological Term Annotation§(ABTA) system which uses classification methods to§annotate terms in text. A novel method is presented§to express lexical features in pattern notations.§Prefix and suffix characters are used instead of§lists of potential terms or external resources. We§demonstrate that part-of-speech tag information is§the most effective attribute. Creating classification§exemplars is conducted from text by using word n-gram§model. We illustrate improvements on our system''s§performance which depends on the feature attributes§we define. Biological concept markers are also§assigned to each located term indicating its meaning.§Our results are comparable to the performance of§other existing systems while our system retains§simplicity and generalizability.

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About the book

Full name Automatic Biological Term Annotation
Language English
Binding Book - Paperback
Date of issue 2009
Number of pages 96
EAN 9783639107333
Libristo code 06819675
Weight 159
Dimensions 150 x 220 x 6
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