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BioInfer: a corpus for information extraction in the biomedical domain.

Pyysalo S, Ginter F, Heimonen J, Björne J, Boberg J, Järvinen J, Salakoski T - BMC Bioinformatics (2007)

Bottom Line: Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies.Supporting software is provided with the corpus.We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers.

View Article: PubMed Central - HTML - PubMed

Affiliation: Turku Centre for Computer Science (TUCS), University of Turku, Lemminkäisenkatu 14a, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi

ABSTRACT

Background: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora.

Results: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation.

Conclusion: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer.

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Layered coordination annotated using the LG approach. Coordinations with and as constituents in a coordination with or.
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Figure 8: Layered coordination annotated using the LG approach. Coordinations with and as constituents in a coordination with or.

Mentions: Capturing coordination in the dependency syntax framework is not straightforward and there exist various approaches to the problem. The Link Grammar and, for example, the PARC 700 Dependency Bank annotation [44], capture coordination using a structure in which the coordinated elements are dependents of the coordinator, which in turn represents the functional role of each of the coordinates. In contrast, the Connexor Machinese Syntax parser, for example, chains the coordinated elements and the head of the chain shows the functional role of the coordinated units, while the coordinator is a mere dependent of one of the elements in the chain. See Figures 8 and 9 for illustration of the structures discussed.


BioInfer: a corpus for information extraction in the biomedical domain.

Pyysalo S, Ginter F, Heimonen J, Björne J, Boberg J, Järvinen J, Salakoski T - BMC Bioinformatics (2007)

Layered coordination annotated using the LG approach. Coordinations with and as constituents in a coordination with or.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1808065&req=5

Figure 8: Layered coordination annotated using the LG approach. Coordinations with and as constituents in a coordination with or.
Mentions: Capturing coordination in the dependency syntax framework is not straightforward and there exist various approaches to the problem. The Link Grammar and, for example, the PARC 700 Dependency Bank annotation [44], capture coordination using a structure in which the coordinated elements are dependents of the coordinator, which in turn represents the functional role of each of the coordinates. In contrast, the Connexor Machinese Syntax parser, for example, chains the coordinated elements and the head of the chain shows the functional role of the coordinated units, while the coordinator is a mere dependent of one of the elements in the chain. See Figures 8 and 9 for illustration of the structures discussed.

Bottom Line: Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies.Supporting software is provided with the corpus.We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers.

View Article: PubMed Central - HTML - PubMed

Affiliation: Turku Centre for Computer Science (TUCS), University of Turku, Lemminkäisenkatu 14a, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi

ABSTRACT

Background: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora.

Results: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation.

Conclusion: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer.

Show MeSH