3260 papers • 126 benchmarks • 313 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.
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A gold standard corpus of time expressions in EHRs for patients with schizophrenia is developed, compared to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions.
This paper presents an approach to extend HeidelTime to all languages in the world, and shows promising results, in particular considering that this approach neither requires language skills nor training data, but results in a baseline tagger for 200+ languages.
CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model is presented.
This study employs a structured perceptron together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level.
The authors' system performed above average for all subtasks in both phases of Clinical TempEval 2017, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations.
A new approach to obtain temporal relations from absolute time value (a.k.a. time anchors), which is suitable for texts containing rich temporal information such as news articles and requires less annotation effort, can induce inter-sentence relations easily, and increases informativeness of temporal relations.
This paper presents TIMEPLEX, a novel time-aware KBC method, that also automatically exploits the recurrent nature of some relations and temporal interactions between pairs of relations, and achieves state-of-the-art performance on both prediction tasks.
The authors present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules, and demonstrate Trove’s ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.
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