BioNLP Shared Task--The Bacteria Track.

BACKGROUND: We present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming supporting task is aimed at extracting gene renaming and gene name synonymy in PubMed abstracts. The Bacteria Gene Interaction is a gene/protein interaction extraction task from individual sentences. The interactions have been categorized into ten different sub-types, thus giving a detailed account of genetic regulations at the molecular level. Finally, the Bacteria Biotopes task focuses on the localization and environment of bacteria mentioned in textbook articles. We describe the process of creation for the three corpora, including document acquisition and manual annotation, as well as the metrics used to evaluate the participants' submissions. RESULTS: Three teams submitted to the Bacteria Gene Renaming task; the best team achieved an F-score of 87%. For the Bacteria Gene Interaction task, the only participant's score had reached a global F-score of 77%, although the system efficiency varies significantly from one sub-type to another. Three teams submitted to the Bacteria Biotopes task with very different approaches; the best team achieved an F-score of 45%. However, the detailed study of the participating systems efficiency reveals the strengths and weaknesses of each participating system. CONCLUSIONS: The three tasks of the Bacteria Track offer participants a chance to address a wide range of issues in Information Extraction, including entity recognition, semantic typing and coreference resolution. We found common trends in the most efficient systems: the systematic use of syntactic dependencies and machine learning. Nevertheless, the originality of the Bacteria Biotopes task encouraged the use of interesting novel methods and techniques, such as term compositionality, scopes wider than the sentence.

Learning ontological rules to extract multiple relations of genic interactions from text.

INTRODUCTION: Information extraction (IE) systems have been proposed in recent years to extract genic interactions from bibliographical resources. They are limited to single interaction relations, and have to face a trade-off between recall and precision, by focusing either on specific interactions (for precision), or general and unspecified interactions of biological entities (for recall). Yet, biologists need to process more complex data from literature, in order to study biological pathways. An ontology is an adequate formal representation to model this sophisticated knowledge. However, the tight integration of IE systems and ontologies is still a current research issue, a fortiori with complex ones that go beyond hierarchies. METHOD: We propose a rich modeling of genic interactions with an ontology, and show how it can be used within an IE system. The ontology is seen as a language specifying a normalized representation of text. First, IE is performed by extracting instances from natural language processing (NLP) modules. Then, deductive inferences on the ontology language are completed, and new instances are derived from previously extracted ones. Inference rules are learnt with an inductive logic programming (ILP) algorithm, using the ontology as the hypothesis language, and its instantiation on an annotated corpus as the example language. Learning is set in a multi-class setting to deal with the multiple ontological relations. RESULTS: We validated our approach on an annotated corpus of gene transcription regulations in the Bacillus subtilis bacterium. We reach a global recall of 89.3% and a precision of 89.6%, with high scores for the ten semantic relations defined in the ontology.