Automatic Term Identi cation and Classi cation in Biology Texts

The rapid growth of collections in online academic databases has meant that there is increasing di culty for experts who want to access information in a timely and e cient way. We seek here to explore the application of information extraction methods to the identi cation and classi cation of terms in biological abstracts from MEDLINE. We explore the use of a statistical method and a decision tree method for classi cation and term candidate identi cation and also a method based on shallow parsing for identi cation. Experiments are made against a corpus of 100 expert tagged abstracts and results indicate that while identifying term boundaries is non-trivial, a high success rate can be obtained in term classi cation and that a combination of methods will provide the best solution.