SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations

By N. UzZaman, H. Llorens, Leon Derczynski, James F. Allen, Marc Verhagen, James Pustejovsky. 2013

462 citations recorded by the source corpus.

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Citations in the corpus

ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “5371 research by using a trigger word (often a verb or an noun that most clearly describes the event’s occurrence) to represent an event (UzZaman et al., 2013; Glavaš et al., 2014; O’Gorman et al., 2016).”
Evaluating NLP Models via Contrast Sets (2020, methodology): “The event annotations are taken from the data provided in the TempEval3 workshop (UzZaman et al., 2013) and the temporal relations are re-annotated based on a multi-axis formalism.”
Extracting Human Temporal Orientation from Facebook Language (2015, background): “The SemEval competitions have provided data sets that facilitate the comparison of different meth-ods for evaluating time expressions, events, and temporal relations (Verhagen et al., 2007; Verhagen et al., 2010; UzZaman et al., 2013).”
Temporal Common Sense Acquisition with Minimal Supervision (2020, background): “inference, and QA (UzZaman et al., 2013; Chambers et al., 2014; Llorens et al., 2015; Bethard et al., 2016; Leeuwenberg and Moens, 2017; Ning et al., 2018b), rely on understanding time.”
Deep Structured Neural Network for Event Temporal Relation Extraction (2019, methodology): “For more details of this metric, please refer to the original paper (UzZaman et al., 2013).”
SemEval-2016 Task 12: Clinical TempEval (2016, background): “The TempEval shared tasks have, since 2007, provided a focus for research on temporal information extraction (Verhagen et al., 2007; Verhagen et al., 2010; UzZaman et al., 2013).”
Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer (2021, unknown): “A lot of temporal relation extraction benchmarks have been developed over the past years (Puste-jovsky et al., 2003; UzZaman et al., 2013; Cas-sidy et al., 2014b; Ning et al., 2018; Vashishtha et al., 2019; Ning et al., 2020), among which, MA-TRES and TimeBank-Dense(TB-Dense) are the two largest…”
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (2018, unknown): “The task of extracting temporally rich events and time expressions and ordering between them is introduced in TempEval challenge (UzZaman et al., 2013; Verhagen et al., 2010).”
SemEval-2017 Task 12: Clinical TempEval (2017, methodology): “The TempEval shared tasks have, since 2007, provided a focus for research on temporal information extraction (Verhagen et al., 2007, 2010; UzZaman et al., 2013).”
Joint Constrained Learning for Event-Event Relation Extraction (2020, methodology): “MATRES is a new benchmark dataset for Tem-pRel extraction, which is developed from TempE-val3 (UzZaman et al., 2013).”
Neural Models of Factuality (2018, methodology): “The smaller MEAN-TIME corpus (Minard et al., 2016) Lee et al. (2015) construct an event factuality dataset – henceforth, UW – on the TempEval-3 data (UzZaman et al., 2013) using crowdsourced annotations on a [ − 3 , 3] scale ( certainly did not happen to certainly did ), with over 13,000 predicates.”
Partial Or Complete, That’s The Question (2019, unknown): “4: temporal relation (TempRel) extraction (UzZaman et al., 2013), semantic role classification (SRC),1”
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (2019, methodology): “Improvements on event extraction can be difﬁcult because our single-task model already works quite well with a close-to 89% F1 score, while the inter-annotator agreement for events in TimeBank documents is merely 87% (UzZaman et al., 2013).”
Joint Reasoning for Temporal and Causal Relations (2018, methodology): “Multiple datasets with temporal annotations are available thanks to the TempEval (TE) workshops (Verhagen et al., 2007, 2010; UzZaman et al., 2013).”
Resource Interoperability for Sustainable Benchmarking: The Case of Events (2018, methodology): “UzZaman et al. (2013) mention that TE3 borrowed the events from TempEval-2 and added missing events, but no example is given.”
HiEve: A Corpus for Extracting Event Hierarchies from News Stories (2014, methodology): “Much of this work was carried out within designated evaluation campaigns such as Automated Content Extraction (ACE) (ACE, 2005; ACE, 2007) and TempEval (Verha-gen et al., 2007; Verhagen et al., 2010; UzZaman et al., 2013).”
A Multi-Axis Annotation Scheme for Event Temporal Relations (2018, background): “TempEval1-3 (Verhagen et al., 2007, 2010; UzZaman et al., 2013), TimeBank-Dense (TB-Dense) (Cassidy et al.”