ProtChew: Automatic Extraction of Protein Names from Biomedical Literature

Abstract

With the increasing amount of biomedical literature, there is a need for automatic extraction of information to support biomedical researchers. Due to incomplete biomedical information databases, the extraction is not straightforward using dictionaries, and several approaches using contextual rules and machine learning have previously been proposed. Our work is inspired by the previous approaches, but is novel in the sense that it is fully automatic and doesn't rely on expert tagged corpora. The main ideas are 1) unigram tagging of corpora using known protein names for training examples for the protein name extraction classifier and 2) tight positive and negative examples by having protein-related words as negative examples and protein names/synonyms as positive examples. We present preliminary results on Medline abstracts about gastrin, further work will be on testing the approach on BioCreative benchmark data sets.

Automatic processing of diabetic patients' hospital documentation

“…Although most biomedical IE activities are related to literature mining and terminology extraction, (e.g. (Bunescu et al, 2003), (Tveit and Saetre 2005)), clinical patients record mining is not a new research goal for all languages, e.g. (Hahn, Romacker and Schulz, 2002).…”