Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

ABSTRACT:

Background

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical

SUBMITTER: Akhondi SA 

PROVIDER: S-EPMC4331686 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature