Corpus annotation for mining biomedical events from literature

By Jin-Dong Kim, Tomoko Ohta, Junichi Tsujii. 2008

423 citations recorded by the source corpus.

BackgroundAdvanced 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.ResultsWe 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 Typing, which have collectively contributed to successful completion of a large scale annotation.ConclusionThe resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

brat: a Web-based Tool for NLP-Assisted Text Annotation (2012, methodology): “A variety of previously introduced annotation tools and approaches also served to guide our design decisions, including the fast annotation mode of Knowtator (Ogren, 2006), the search capabilities of the XConc tool (Kim et al., 2008), and the design of web-based systems such as MyMiner (Salgado et a”
Overview of BioNLP Shared Task 2013 (2013, methodology): “Originally the design and implementation of the GE task was based on the Genia event corpus (Kim et al. , 2008) that represents domain knowledge of NF κ B proteins.”
Simple tricks for improving pattern-based information extraction from the biomedical literature (2010, methodology): “The data sets were prepared based on the GENIA event corpus [24].”
The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes (2008, background): “The Genia Event corpus [9], which annotates biological events with negation and three levels of uncertainty (1000 abstracts).”
Concept annotation in the CRAFT corpus (2012, methodology): “…corpora, as these are obviously most directly related: ABGene [42], BioInfer [43], the CLEF Corpus [44], the FetchProt Corpus [45], the Fourth i2b2/VA Challenge corpus [46], GENETAG [47], GENIA [48,49], GREC [50], the ITI TXM PPI and TE corpora [51], MedPost [52], the PennBioIE Oncology and CYP v1.”
Construction of an annotated corpus to support biomedical information extraction (2009, methodology): “We plan to convert our own corpus to this format, which has already been carried out for several biomedical corpora, e.g., [13,16,21,23].”
Overview of BioNLP’09 Shared Task on Event Extraction (2009, methodology): “The data for the training and development sets were derived from the publicly available event corpus (Kim et al., 2008), and the data for the test set from an unpublished portion of the corpus.”
All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning (2008, background): “The public availability of large annotated PPI-corpora such as AImed [4], BioInfer [6] and GENIA [7], provides an opportunity for building PPI extraction systems automatically using machine learning.”