Könyv Automatic Biological Term Annotation Sittichai Jiampojamarn

Automatic Biological Term Annotation

Using n-gram and Classification Models

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Kiadói készleten rendelésre
Küldés 17-27 napon belül
17 503 Ft
Exciting research in biology has resulted in a large§amount of biological publications. §Knowledge d...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2009
oldal
96
EAN
9783639107333
Enbook ID
06819675
Súly
159
Méretek
150 x 220 x 6

Teljes leírás

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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