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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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An expansion of a macro-dependency. Noun phrase with an NP macro-dependency (left), parallel expansion (middle) and serial expansion (right). NP macro-dependencies are depicted as thick lines.
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Figure 7: An expansion of a macro-dependency. Noun phrase with an NP macro-dependency (left), parallel expansion (middle) and serial expansion (right). NP macro-dependencies are depicted as thick lines.

Mentions: In the annotation, an NP macro-dependency is used to connect the leftmost pre-modifier and the head noun. An NP macro-dependency can be expanded automatically to attach all pre-modifiers spanned by the macro-dependency in either of the two manners introduced above, making the annotation applicable to the two most common analyses (see Figure 7). Further, the macro-dependency is used consistently also in cases where there is only one pre-modifier. This allows the mere presence of the macro-dependency to be used as an indicator of a non-elementary noun phrase.


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)

An expansion of a macro-dependency. Noun phrase with an NP macro-dependency (left), parallel expansion (middle) and serial expansion (right). NP macro-dependencies are depicted as thick lines.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: An expansion of a macro-dependency. Noun phrase with an NP macro-dependency (left), parallel expansion (middle) and serial expansion (right). NP macro-dependencies are depicted as thick lines.
Mentions: In the annotation, an NP macro-dependency is used to connect the leftmost pre-modifier and the head noun. An NP macro-dependency can be expanded automatically to attach all pre-modifiers spanned by the macro-dependency in either of the two manners introduced above, making the annotation applicable to the two most common analyses (see Figure 7). Further, the macro-dependency is used consistently also in cases where there is only one pre-modifier. This allows the mere presence of the macro-dependency to be used as an indicator of a non-elementary noun phrase.

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