Evaluating gold standard corpora against gene/protein tagging solutions and lexical resources

Abstract

Motivation The identification of protein and gene names (PGNs) from the scientific literature requires semantic resources: Terminological and lexical resources deliver the term candidates into PGN tagging solutions and the gold standard corpora (GSC) train them to identify term parameters and contextual features.Ideally all three resources, i.e.~corpora, lexica and taggers, cover the same domain knowledge, and thus support identification of the same types of PGNs and cover all of them.Unfortunately, none of the three serves as a predominant standard and for this reason it is worth exploring, how these three resources comply with each other.We systematically compare different PGN taggers against publicly available corpora and analyze the impact of the included lexical resource in their performance.In particular, we determine the performance gains through false positive filtering, which contributes to the disambiguation of identified PGNs. RESULTS: In general, machine learning approaches (ML-Tag) for PGN tagging show higher F1-measureperformance against the BioCreative-II and Jnlpba GSCs (exact matching), whereas the lexicon basedapproaches (LexTag) in combination with disambiguation methods show better results on FsuPrgeand PennBio. The ML-Tag solutions balance precision and recall, whereas the LexTag solutions havedifferent precision and recall profiles at the same F1-measure across all corpora. Higher recall isachieved with larger lexical resources, which also introduce more noise (false positive results). TheML-Tag solutions certainly perform best, if the test corpus is from the same GSC as the trainingcorpus. As expected, the false negative errors characterize the test corpora and - on the other hand- the profiles of the false positive mistakes characterize the tagging solutions. Lex-Tag solutions thatare based on a large terminological resource in combination with false positive filtering produce betterresults, which, in addition, provide concept identifiers from a knowledge source in contrast to ML-Tagsolutions. CONCLUSION: The standard ML-Tag solutions achieve high performance, but not across all corpora, and thus shouldbe trained using several different corpora to reduce possible biases. The LexTag solutions havedifferent profiles for their precision and recall performance, but with similar F1-measure. This resultis surprising and suggests that they cover a portion of the most common naming standards, but copedifferently with the term variability across the corpora. The false positive filtering applied to LexTagsolutions does improve the results by increasing their precision without compromising significantlytheir recall. The harmonisation of the annotation schemes in combination with standardized lexicalresources in the tagging solutions will enable their comparability and will pave the way for a sharedstandard.