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Event-based text mining for biology and functional genomics.

Ananiadou S, Thompson P, Nawaz R, McNaught J, Kell DB - Brief Funct Genomics (2014)

Bottom Line: This article provides an overview of recent research into event extraction.We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems.Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

View Article: PubMed Central - PubMed

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More complex sentence containing multiple events.
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elu015-F4: More complex sentence containing multiple events.

Mentions: Figure 3 shows a more complex example, involving two events. First, the protein IL-10 is identified as the Theme of the simple ‘Expression’ event. The verb ‘upregulates’ is the trigger for the second, complex event, which has been assigned the semantic event type ‘Positive regulation’. This event has two participants. The protein LMP1 has been identified as the ‘Cause’ of the positive regulation event, while the Theme is the previously mentioned Expression event. Figure 4 shows a longer sentence, but illustrates how event structures can encode complex semantics and normalise over different means of linguistic expression (e.g. the two different Expression events).Figure 3:


Event-based text mining for biology and functional genomics.

Ananiadou S, Thompson P, Nawaz R, McNaught J, Kell DB - Brief Funct Genomics (2014)

More complex sentence containing multiple events.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4499874&req=5

elu015-F4: More complex sentence containing multiple events.
Mentions: Figure 3 shows a more complex example, involving two events. First, the protein IL-10 is identified as the Theme of the simple ‘Expression’ event. The verb ‘upregulates’ is the trigger for the second, complex event, which has been assigned the semantic event type ‘Positive regulation’. This event has two participants. The protein LMP1 has been identified as the ‘Cause’ of the positive regulation event, while the Theme is the previously mentioned Expression event. Figure 4 shows a longer sentence, but illustrates how event structures can encode complex semantics and normalise over different means of linguistic expression (e.g. the two different Expression events).Figure 3:

Bottom Line: This article provides an overview of recent research into event extraction.We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems.Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

View Article: PubMed Central - PubMed

No MeSH data available.