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Automatic Biological Term Annotation

Using n-gram and Classification Models

Jezik EngleskiEngleski
Knjiga Meki uvez
Knjiga Automatic Biological Term Annotation Sittichai Jiampojamarn
Libristo kod: 06819675
Nakladnici VDM Verlag Dr. Müller, studeni 2008
Exciting research in biology has resulted in a large§amount of biological publications. §Knowledge d... Cijeli opis
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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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Informacije o knjizi

Puni naziv Automatic Biological Term Annotation
Jezik Engleski
Uvez Knjiga - Meki uvez
Datum izdanja 2009
Broj stranica 96
EAN 9783639107333
Libristo kod 06819675
Težina 159
Dimenzije 150 x 220 x 6
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