Unsupervised gene/protein named entity normalization using automatically extracted dictionaries

Abstract

Gene and protein named-entity recognition (NER) and normalization is often treated as a two-step process. While the first step, NER, has received considerable attention over the last few years, normalization has received much less attention. We have built a dictionary based gene and protein NER and normalization system that requires no supervised training and no human intervention to build the dictionaries from online genomics resources. We have tested our system on the Genia corpus and the BioCreative Task 1B mouse and yeast corpora and achieved a level of performance comparable to state-of-the-art systems that require supervised learning and manual dictionary creation. Our technique should also work for organisms following similar naming conventions as mouse, such as human. Further evaluation and improvement of gene/protein NER and normalization systems is somewhat hampered by the lack of larger test collections and collections for additional organisms, such as human.