Temporal Information Extraction
16 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">
<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
These leaderboards are used to track progress in Temporal Information Extraction
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TempEval-3
Latest papers with no code
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
Context-Aware Neural Model for Temporal Information Extraction
We propose a context-aware neural network model for temporal information extraction.
Chrono at SemEval-2018 Task 6: A System for Normalizing Temporal Expressions
Temporal information extraction is a challenging task.
SemEval-2017 Task 12: Clinical TempEval
Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)?
LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives
In this paper we present our participation to SemEval 2017 Task 12.
Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes
This paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of the 2017 Clinical TempEval challenge.
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction
Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text.
Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
We present a neural architecture for containment relation identification between medical events and/or temporal expressions.
