Difference between revisions of "Temporal ordering"
| (6 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
This is a [[category::problem]] to automatically order events in text based on time. Temporal relation between any two events can be categorized into one of the 13 classes defined over [[TimeBank_Corpus|TimeBank Corpus]]: ''before'', ''ibefore'' (immediately before), ''includes'', ''begins'', ''ends'', ''overlap'' and their inverses, plus ''simultaneous''. | This is a [[category::problem]] to automatically order events in text based on time. Temporal relation between any two events can be categorized into one of the 13 classes defined over [[TimeBank_Corpus|TimeBank Corpus]]: ''before'', ''ibefore'' (immediately before), ''includes'', ''begins'', ''ends'', ''overlap'' and their inverses, plus ''simultaneous''. | ||
| + | |||
| + | For example, (taken from [[Yoshikawa_2009_jointly_identifying_temporal_relations_with_markov_logic|Yoshikawa et al. (2009)]]), consider the following sentence from a document: | ||
| + | |||
| + | :: ''With the introduction of the TimeBank corpus (Pustejovsky et al., 2003), machine learning approaches to temporal ordering became possible.'' | ||
| + | |||
| + | Here, temporal ordering task has to predict that the "Machine Learning becoming possible" event happened ''AFTER'' the "introduction of the TimeBank Corpus" ("event-event" temporal relation) and ''OVERLAP'' with the year 2003 ("event-time expression" temporal relation). Further, the task has to predict that both of these events occur ''BEFORE'' the document containing this sentence is created ("event-document creation time" temporal relation). | ||
== Relevant Papers == | == Relevant Papers == | ||
Latest revision as of 16:38, 29 September 2011
This is a problem to automatically order events in text based on time. Temporal relation between any two events can be categorized into one of the 13 classes defined over TimeBank Corpus: before, ibefore (immediately before), includes, begins, ends, overlap and their inverses, plus simultaneous.
For example, (taken from Yoshikawa et al. (2009)), consider the following sentence from a document:
- With the introduction of the TimeBank corpus (Pustejovsky et al., 2003), machine learning approaches to temporal ordering became possible.
Here, temporal ordering task has to predict that the "Machine Learning becoming possible" event happened AFTER the "introduction of the TimeBank Corpus" ("event-event" temporal relation) and OVERLAP with the year 2003 ("event-time expression" temporal relation). Further, the task has to predict that both of these events occur BEFORE the document containing this sentence is created ("event-document creation time" temporal relation).
