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# Temporal Information Extraction Edit

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 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>

</TEXT>
</TimeML>


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

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# Study of lexical aspect in the French medical language. Development of a lexical resource

This paper details the development of a linguistic resource designed to improve temporal information extraction systems and to integrate aspectual values.

# Temporal Information Extraction by Predicting Relative Time-lines

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.

# Neural Ranking Models for Temporal Dependency Structure Parsing

In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0. 81 and 0. 70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches.

# Investigating the Challenges of Temporal Relation Extraction from Clinical Text

Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain.

# 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.

# Temporal Information Extraction by Predicting Relative Time-lines

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.

# 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.

# RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses

Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media.