SOTAVerified

Temporal Information Extraction

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.







 PRI20001020.2000.0127 
 NEWS STORY 
 10/20/2000 20:02:07.85 


 The Navy has changed its account of the attack on the USS Cole in Yemen.
 Officials now say the ship was hit nearly two hours 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.


 10/20/2000 20:02:28.05 






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

Papers

Showing 4150 of 86 papers

TitleStatusHype
Structured Learning for Temporal Relation Extraction from Clinical RecordsCode0
Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture0
CATENA: CAusal and TEmporal relation extraction from NAtural language textsCode0
Timeline extraction using distant supervision and joint inference0
Inferring Methodological Meta-knowledge from Large Biomedical Corpora0
Improving Temporal Relation Extraction with Training Instance Augmentation0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction0
UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text0
Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Ning et al.Temporal awareness67.2Unverified
2ClearTKTemporal awareness30.98Unverified
#ModelMetricClaimedVerifiedStatus
1CatenaF1 score0.51Unverified
2CAEVOF1 score0.51Unverified