Biological nomenclatures: a source of lexical knowledge and ambiguity.
There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.
Department of Biomedical Informatics, Columbia University, 622 W 168 St, VC-5, New York, NY 10032, USA.
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D001185:Artificial Intelligence; D019295:Computational Biology; D030541:Databases, Genetic; D023281:Genomics / Q000706:statistics & numerical data; D008957:Models, Genetic; D009323:Natural Language Processing; D009626:Terminology as Topic