Weakly supervised learning methods for improving the quality of gene name normalization data

Abstract

A pervasive problem facing many biomedical text mining applications is that of correctly associating mentions of entities in the literature with corresponding concepts in a database or ontology. Attempts to build systems for automating this process have shown promise as demonstrated by the recent BioCreAtIvE Task 1B evaluation. A significant obstacle to improved performance for this task, however, is a lack of high quality training data. In this work, we explore methods for improving the quality of (noisy) Task 1B training data using variants of weakly supervised learning methods. We present positive results demonstrating that these methods result in an improvement in training data quality as measured by improved system performance over the same system using the originally labeled data.