Overview of BioCreAtIvE: critical assessment of information extraction for biology View Full Text


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Article Info

DATE

2005-05

AUTHORS

Lynette Hirschman, Alexander Yeh, Christian Blaschke, Alfonso Valencia

ABSTRACT

BACKGROUND: The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28-31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation. RESULTS: BioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles. CONCLUSION: The first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2. More... »

PAGES

s1

References to SciGraph publications

  • 2000-05. Gene Ontology: tool for the unification of biology in NATURE GENETICS
  • 2005-05. Mining protein function from text using term-based support vector machines in BMC BIOINFORMATICS
  • 2005-05. Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot in BMC BIOINFORMATICS
  • 2005-05. Protein annotation as term categorization in the gene ontology using word proximity networks in BMC BIOINFORMATICS
  • 2005-05. Finding genomic ontology terms in text using evidence content in BMC BIOINFORMATICS
  • 2005-05. Systematic feature evaluation for gene name recognition in BMC BIOINFORMATICS
  • 2005-05. Gene/protein name recognition based on support vector machine using dictionary as features in BMC BIOINFORMATICS
  • 2005-05. GENETAG: a tagged corpus for gene/protein named entity recognition in BMC BIOINFORMATICS
  • 2005-05. BioCreAtIvE Task 1A: gene mention finding evaluation in BMC BIOINFORMATICS
  • 2005-05. Recognition of protein/gene names from text using an ensemble of classifiers in BMC BIOINFORMATICS
  • 2005-05. Identifying gene and protein mentions in text using conditional random fields in BMC BIOINFORMATICS
  • 2005-05. Exploring the boundaries: gene and protein identification in biomedical text in BMC BIOINFORMATICS
  • 2005-05. BioCreAtIvE Task1A: entity identification with a stochastic tagger in BMC BIOINFORMATICS
  • 2005-05. A sentence sliding window approach to extract protein annotations from biomedical articles in BMC BIOINFORMATICS
  • 2005-05. Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text in BMC BIOINFORMATICS
  • 2005-05. An evaluation of GO annotation retrieval for BioCreAtIvE and GOA in BMC BIOINFORMATICS
  • 2005-05. Evaluation of BioCreAtIvE assessment of task 2 in BMC BIOINFORMATICS
  • 2005-05. A simple approach for protein name identification: prospects and limits in BMC BIOINFORMATICS
  • 2005-05. ProMiner: rule-based protein and gene entity recognition in BMC BIOINFORMATICS
  • 2005-05. Automatically annotating documents with normalized gene lists in BMC BIOINFORMATICS
  • 2005-05. Data preparation and interannotator agreement: BioCreAtIvE Task 1B in BMC BIOINFORMATICS
  • 2005-05. Overview of BioCreAtIvE task 1B: normalized gene lists in BMC BIOINFORMATICS
  • 2005-05. Text Detective: a rule-based system for gene annotation in biomedical texts in BMC BIOINFORMATICS
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