Corpus annotation for mining biomedical events from literature

BMC Bioinformatics
Jin-Dong KimJun'ichi Tsujii

Abstract

Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic...Continue Reading

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Datasets Mentioned

BETA
GM-CSF

Methods Mentioned

BETA
transfection
PMA
flow
electrophoretic

Software Mentioned

GENIA
BioInfer
SBML
GENETAG
JDK
PropBank
XConc Suite
PennBioIE
Eclipse
XConc ( XML based Concordancer ) Suite

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