Evaluation and cross-comparison of lexical entities of biological interest (LexEBI).

Motivation: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e.

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

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.

Monitoring named entity recognition: the League Table.

Background: Named entity recognition (NER) is an essential step in automatic text processing pipelines. A number of solutions have been presented and evaluated against gold standard corpora (GSC). The benchmarking against GSCs is crucial, but left to the individual researcher.

Harmonization of gene/protein annotations: towards a gold standard MEDLINE.

Motivation: The recognition of named entities (NER) is an elementary task in biomedical text mining. A number of NER solutions have been proposed in recent years, taking advantage of available annotated corpora, terminological resources and machine-learning techniques. Currently, the best performing solutions combine the outputs from selected annotation solutions measured against a single corpus.

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus.

Background: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I).

UKPMC: a full text article resource for the life sciences.

UK PubMed Central (UKPMC) is a full-text article database that extends the functionality of the original PubMed Central (PMC) repository. The UKPMC project was launched as the first 'mirror' site to PMC, which in analogy to the International Nucleotide Sequence Database Collaboration, aims to provide international preservation of the open and free-access biomedical literature. UKPMC (http://ukpmc.

Natural language processing in aid of FlyBase curators.

Background: Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear.

Results: PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser.

Integrating natural language processing with FlyBase curation.

Applying Natural Language Processing techniques to biomedical text as a potential aid to curation has become the focus of intensive research. However, developing integrated systems which address the curators' real-world needs has been studied less rigorously. This paper addresses this question and presents generic tools developed to assist FlyBase curators.

Bootstrapping the recognition and anaphoric linking of named entities in Drosophila articles.

This paper demonstrates how Drosophila gene name recognition and anaphoric linking of gene names and their products can be achieved using existing information in FlyBase and the Sequence Ontology. Extending an extant approach to gene name recognition we achieved a F-score of 0.8559, and we report a preliminary experiment using a baseline anaphora resolution algorithm.