Temporal Information Extraction

11 papers with code • 2 benchmarks • 3 datasets

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">

 <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.

Most implemented papers

Time Expressions in Mental Health Records for Symptom Onset Extraction

medesto/annotation-guidelines WS 2018

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.

GATE-Time: Extraction of Temporal Expressions and Events

GateNLP/gateplugin-Tagger_GATE-TIME LREC 2016

GATE is a widely used open-source solution for text processing with a large user community.

CATENA: CAusal and TEmporal relation extraction from NAtural language texts

paramitamirza/CATENA COLING 2016

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.

Structured Learning for Temporal Relation Extraction from Clinical Records

tuur/SPTempRels EACL 2017

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).

Deep Learning for Hand Gesture Recognition on Skeletal Data

guillaumephd/deep_learning_hand_gesture_recognition IEEE FG 2018 2018

In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.

Inducing Temporal Relations from Time Anchor Annotation

racerandom/temporalorder NAACL 2018

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

Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols

dair-iitd/tkbi EMNLP 2020

Temporal knowledge bases associate relational (s, r, o) triples with a set of times (or a single time instant) when the relation is valid.

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

som-shahlab/trove 5 Aug 2020

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.