Temporal Information Extraction Edit Task
Natural Language Processing • Temporal Processing
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 |
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Datasets
Greatest papers with code
CATENA: CAusal and TEmporal relation extraction from NAtural language texts
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.
Ranked #1 on
Temporal Information Extraction
on TimeBank
QUESTION ANSWERING RELATION CLASSIFICATION TEMPORAL INFORMATION EXTRACTION
Deep Learning for Hand Gesture Recognition on Skeletal Data
IEEE FG 2018 2018
• guillaumephd/deep_learning_hand_gesture_recognition
• 
In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
3D SHAPE CLASSIFICATION HAND-GESTURE RECOGNITION SKELETON BASED ACTION RECOGNITION TEMPORAL INFORMATION EXTRACTION
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.
Ranked #1 on
Link Prediction
on Wikidata12k
KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH COMPLETION LINK PREDICTION TEMPORAL INFORMATION EXTRACTION TEMPORAL KNOWLEDGE GRAPH COMPLETION TIME-INTERVAL PREDICTION
Structured Learning for Temporal Relation Extraction from Clinical Records
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).
Towards Extracting Absolute Event Timelines From English Clinical Reports
Temporal information extraction is a challenging but important area of automatic natural language understanding.
NATURAL LANGUAGE UNDERSTANDING TEMPORAL INFORMATION EXTRACTION
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
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.
KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records
In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017.
RELATION EXTRACTION TEMPORAL INFORMATION EXTRACTION UNSUPERVISED DOMAIN ADAPTATION