About

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 www.timeml.org.

<?xml version="1.0" ?>

<TimeML xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://timeml.org/timeMLdocs/TimeML_1.2.1.xsd">
<TEXT>


 PRI20001020.2000.0127 
 NEWS STORY 
 <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"/>
</TEXT>
</TimeML>

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

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Datasets

Latest papers with code

Towards Extracting Absolute Event Timelines From English Clinical Reports

28 Sep 2020tuur/AbsClinTimelinesTASL

Temporal information extraction is a challenging but important area of automatic natural language understanding.

NATURAL LANGUAGE UNDERSTANDING TEMPORAL INFORMATION EXTRACTION

0
28 Sep 2020

Ontology-driven weak supervision for clinical entity classification in electronic health records

5 Aug 2020som-shahlab/trove

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.

CLASSIFICATION NAMED ENTITY RECOGNITION TEMPORAL INFORMATION EXTRACTION WEAKLY SUPERVISED CLASSIFICATION WEAKLY-SUPERVISED NAMED ENTITY RECOGNITION

18
05 Aug 2020

Time Expressions in Mental Health Records for Symptom Onset Extraction

WS 2018 medesto/annotation-guidelines

Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these.

TEMPORAL INFORMATION EXTRACTION

0
01 Oct 2018

Temporal Information Extraction by Predicting Relative Time-lines

EMNLP 2018 tuur/PredRelTimelines

The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations.

TEMPORAL INFORMATION EXTRACTION

0
28 Aug 2018

Inducing Temporal Relations from Time Anchor Annotation

NAACL 2018 racerandom/temporalorder

Conventional annotation of judging temporal relations puts a heavy load on annotators.

QUESTION ANSWERING TEMPORAL INFORMATION EXTRACTION

0
01 Jun 2018

Structured Learning for Temporal Relation Extraction from Clinical Records

EACL 2017 tuur/SPTempRels

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

DOCUMENT-LEVEL RELATION EXTRACTION TEMPORAL INFORMATION EXTRACTION

3
01 Apr 2017

CATENA: CAusal and TEmporal relation extraction from NAtural language texts

COLING 2016 paramitamirza/CATENA

The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.

CLASSIFICATION QUESTION ANSWERING RELATION CLASSIFICATION TEMPORAL INFORMATION EXTRACTION

30
01 Dec 2016