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Automatic Biological Term Annotation

Name: Automatic Biological Term Annotation
Brand: VDM Verlag Dr. Müller
SKU: 9783639107333
Price: 254.13 RON
Availability: InStock

Using n-gram and Classification Models

Autor: Sittichai Jiampojamarn

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Editura: VDM Verlag Dr. Müller

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Exciting research in biology has resulted in a large§amount of biological publications. §Knowledge d...

Informații despre carte

Autor

Sittichai Jiampojamarn

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2009

Pagini

EAN

9783639107333

Enbook ID

06819675

Editura

VDM Verlag Dr. Müller

Greutate

159

Dimensiuni

150 x 220 x 6

Descriere completă

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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Automatic Biological Term Annotation

Using n-gram and Classification Models

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