Meta-Knowledge Annotation at the Event Level: Comparison between Abstracts and Full Papers

Biomedical literature contains rich information about events of biological relevance. Event corpora, containing classified, structured representations of important facts and findings contained within text, provide an important resource for the training of domain-specific information extraction (IE) systems. Such corpora pay little attention to the interpretation of events, e.g., whether an event describes a fact or an analysis of results, whether there is any speculation surrounding the event, etc. These types of information are collectively referred to as meta-knowledge. As previous work, an annotation scheme to enrich event corpora with meta-knowledge was designed to facilitate the training of more sophisticated IE systems, and was applied to the complete GENIA Event corpus of biomedical abstracts. In this paper, we describe a case study in which four full papers annotated with GENIA events have been manually enriched with meta-knowledge annotation. We analyse the annotation results, and compare them with the previously annotated abstracts.

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