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
SoftTiger: A Clinical Foundation Model for Healthcare Workflows
We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows.
tieval: An Evaluation Framework for Temporal Information Extraction Systems
All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems.
Towards Extracting Absolute Event Timelines From English Clinical Reports
Temporal information extraction is a challenging but important area of automatic natural language understanding.
Ontology-driven weak supervision for clinical entity classification in electronic health records
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.
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
Temporal knowledge bases associate relational (s, r, o) triples with a set of times (or a single time instant) when the relation is valid.
#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended).
Time Expressions in Mental Health Records for Symptom Onset Extraction
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 by Predicting Relative Time-lines
tuur/PredRelTimelines
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EMNLP 2018
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.
Inducing Temporal Relations from Time Anchor Annotation
Conventional annotation of judging temporal relations puts a heavy load on annotators.
Deep Learning for Hand Gesture Recognition on Skeletal Data
guillaumephd/deep_learning_hand_gesture_recognition
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IEEE FG 2018 2018
In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
