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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