Event Extraction Task
An Event Extraction Task is an Information Extraction task that is restricted to the extraction (identification/classification) of Events.
- AKA: Event Extraction, Event Mention Extraction Task, Event Mention Recognition Task.
- Context:
- Example(s):
- See: Information Extraction Task.
References
2014
- http://www.nist.gov/tac/2014/KBP/Event/
- QUOTE: The goal of the TAC KBP Event track is to extract information about events such that the information would be suitable as input to a knowledge base. The Event Argument Extraction task aims to extract information about entities (and times) and the role they play in an event. Participating systems will extract tuples that include (EventType, Role, Argument). EventType and Role will be drawn from an externally specified ontology that is based on ACE 2005. Arguments will be strings from within a document representing the canonical (most-specific) name or description of the entity.
While this task does not require the reification of events (or linking the different arguments of an event), systems developed for this task will support KB-queries that link entities to participation in an event-- for example "List ORGANIZATIONs with a PURCHASER role". When combined with other KBP technologies (i.e. slot-filling and entity-linking), more complex, multi-hop queries are possible-- for example, "List ORGANIZATIONSs with MEMBERs who have served as an ATTACKER".
This is a new task in 2014 and will be evaluated in English only. In 2015, we expect to extend to additional TAC languages and add an evaluation of a system's ability to reify events and connect their arguments.
- QUOTE: The goal of the TAC KBP Event track is to extract information about events such that the information would be suitable as input to a knowledge base. The Event Argument Extraction task aims to extract information about entities (and times) and the role they play in an event. Participating systems will extract tuples that include (EventType, Role, Argument). EventType and Role will be drawn from an externally specified ontology that is based on ACE 2005. Arguments will be strings from within a document representing the canonical (most-specific) name or description of the entity.
2003
- (Grishman, 2003) ⇒ Ralph Grishman. (2003). “Information Extraction.” In: (Mitkov, 2003).
- We now consider a more complex task: extracting all the instances of a particular type of relationship or event from text. For example, we may have a file of seminar announcements and want to build a table listing the speaker, title, date, time, and location of each seminar.