Background: Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Results: Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare. Biomedical text mining [1-3] has focussed largely on recognising relevant biomedical entities and binary relations between these entities (e.g., protein-protein interactions [4, 5] , gene-disease associations [6, 7] , etc.). However, the extraction of biomedical events from the literature Full list of author information is available at the end of the article has been a recent focus of research into biomedical natural language processing, since events are crucial for understanding biomedical processes and functions [3] . Events constitute structured representations of biomedical knowledge. They are usually organised around verbs (e.g., activate, inhibit) or nominalised verbs (e.g., expression), which we call trigger expressions. Events have arguments, which contribute towards the description of the event. These arguments, which can either be entities (e.g., p53) or other events, are often assigned semantic roles, which characterise the contribution of the argument to
REF use a machine learning-based approach to assign metaknowledge categories to events.
Extracting semantically enriched events from biomedical literature
BMC Bioinformatics
BMC Bioinformatics
Medicine Computer Science