Machine Learning Department
School of Computer Science, Carnegie Mellon University


Bootstrapping Biomedical Ontologies for
Scientific Text using NELL

Dana Movshovitz-Attias, William W. Cohen

May 2012


Keywords: Bootstrap Learning, Semi-Supervised Learning, Information Extraction, Pointwise Mutual Information

We describe an open information extraction system for biomedical text based on NELL (the Never-Ending Language Learner) [7], a system designed for extraction fromWeb text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of "seeds" for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL’s bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL's original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.

26 pages

SCS Technical Report Collection
School of Computer Science