Browse > Natural Language Processing > Information Extraction > Temporal Information Extraction

Temporal Information Extraction

6 papers with code · Natural Language Processing

Temporal information extraction is the identification of chunks/tokens corresponding to temporal intervals, and the extraction and determination of the temporal relations between those. The entities extracted may be temporal expressions (timexes), eventualities (events), or auxiliary signals that support the interpretation of an entity or relation. Relations may be temporal links (tlinks), describing the order of events and times, or subordinate links (slinks) describing modality and other subordinative activity, or aspectual links (alinks) around the various influences aspectuality has on event structure.

The markup scheme used for temporal information extraction is well-described in the ISO-TimeML standard, and also on

<?xml version="1.0" ?>

<TimeML xmlns:xsi="" xsi:noNamespaceSchemaLocation="">

 <TIMEX3 tid="t0" type="TIME" value="2000-10-20T20:02:07.85">10/20/2000 20:02:07.85</TIMEX3> 

 The Navy has changed its account of the attack on the USS Cole in Yemen.
 Officials <TIMEX3 tid="t1" type="DATE" value="PRESENT_REF" temporalFunction="true" anchorTimeID="t0">now</TIMEX3> say the ship was hit <TIMEX3 tid="t2" type="DURATION" value="PT2H">nearly two hours </TIMEX3>after it had docked.
 Initially the Navy said the explosion occurred while several boats were helping
 the ship to tie up. The change raises new questions about how the attackers
 were able to get past the Navy security.

 <TIMEX3 tid="t3" type="TIME" value="2000-10-20T20:02:28.05">10/20/2000 20:02:28.05</TIMEX3> 

<TLINK timeID="t2" relatedToTime="t0" relType="BEFORE"/>

To avoid leaking knowledge about temporal structure, train, dev and test splits must be made at document level for temporal information extraction.

State-of-the-art leaderboards

Greatest papers with code

Structured Learning for Temporal Relation Extraction from Clinical Records

EACL 2017 tuur/SPTempRels

We propose a scalable structured learning model that jointly predicts temporalrelations between events and temporal expressions (TLINKS), and the relationbetween these events and the document creation time (DCTR).


Time Expressions in Mental Health Records for Symptom Onset Extraction

WS 2018 medesto/systems-adaptation

For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes.