Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

摘要

Retrieving information about highly ambiguous gene/protein homonyms is a challenge, in particular where their non-protein meanings are more frequent than their protein meaning (e. g., SAH or HF). Due to their limited coverage in common benchmarking data sets, the performance of existing gene/protein recognition tools on these problematic cases is hard to assess. We uniformly sample a corpus of eight ambiguous gene/protein abbreviations from Medline® and provide manual annotations for each mention of these abbreviations.1 Based on this resource, we show that available gene recognition tools such as conditional random fields (CRF) trained on BioCreative 2 NER data or GNAT tend to underperform on this phenomenon. We propose to extend existing gene recognition approaches by combining a CRF and a support vector machine. In a cross-entity evaluation and without taking any entity-specific information into account, our model achieves a gain of 6 points F_1-Measure over our best baseline which checks for the occurrence of a long form of the abbreviation and more than 9 points over all existing tools investigated.