TimeML: Robust Specification of Event and Temporal Expressions in Text

By James Pustejovsky, J. Castaño, Robert Ingria, Roser Saurí, Robert J. Gaizauskas, A. Setzer, G. Katz, Dragomir R. Radev. 2003

933 citations recorded by the source corpus.

Subfields: Natural Language Processing

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Citations in the corpus

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories (2016, background): “It is important to note that this speciﬁc set rarely had any linguistic adverbials such as ‘ﬁrst’, ‘then’, etc. to help human infer the ordering, so the main factors at play are the following: (1) the commonsensical temporal and causal relation between events (narrative schemas), e.g., human knows…”
Reasoning about Quantities in Natural Language (2015, background): “There has also been some work on quantities in specific domains, such as the temporal domain, the most significant being the TimeML project (Pustejovsky et al., 2003; Saur et al., 2005; PrattHartmann, 2005; Do et al., 2012).”
Eviza: A Natural Language Interface for Visual Analysis (2016, methodology): “We developed temporal functions based on the temporal entities and expressions deﬁned by TimeML[31].”
A Multi-Axis Annotation Scheme for Event Temporal Relations (2018, methodology): “Initiated by TimeBank (TB) (Pustejovsky et al., 2003b), a number of TempRel datasets have been collected, including but not limited to the verbclause augmentation to TB (Bethard et al., 2007), 1The dataset is publicly available at https:// cogcomp.org/page/publication_view/834.”
New Regularized Algorithms for Transductive Learning (2009, methodology): “As many other related algorithms [17] [12] [5], Adsorption assumes that the learning problem is given in a graph form, where examples or instances are represented as nodes or vertices and edges code similarity between examples.”
Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation (2016, methodology): “The second source of “narrative container” annotation is a focus in the annotations on capturing temporal structure using CONTAINS ( INCLUDES , in ISO-TimeML) relations between events and on capturing event-time relationships.”
Evita: A Robust Event Recognizer For QA Systems (2005, background): “Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 700–707, Vancouver, October 2005. c©2005 Association for Computational Linguistics”
SemEval-2007 Task 15: TempEval Temporal Relation Identification (2007, background): “TimeBank (Pustejovsky et al., 2003b; Boguraev et al., forthcoming) was originally conceived of as a proof of concept that illustrates the TimeML language, but has since gone through several rounds of revisions and can now be considered a gold standard for temporal information.”
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding (2019, background): “TimeBank (Pustejovsky et al., 2003b) is an example with events and their relations annotated sparsely.”
SUTime: A library for recognizing and normalizing time expressions (2012, background): “It collects invited chapters and extended research contributions from DART 2014 (the 8th International Workshop on Information Filtering and Retrieval), held in Pisa (Italy), on December 10, 2014, and co-hosted with the XIII AI*IA Symposium on Artificial Intelligence.”
HiEve: A Corpus for Extracting Event Hierarchies from News Stories (2014, methodology): “The emergence of the TimeML standard (Pustejovsky et al., 2003a) and the TimeML abiding TimeBank corpus (Pustejovsky et al., 2003b) triggered a large body of work on extracting events and temporal information from text.”
CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures (2016, methodology): “One of the existing definitions for event is provided in the TimeML annotation schema (Pustejovsky et al., 2003):”
SituatedQA: Incorporating Extra-Linguistic Contexts into QA (2021, background): “, ordering events or finding temporal relationship between two events in the same document (Pustejovsky et al., 2003; Bethard et al., 2007; Cassidy et al., 2014; Llorens et al., 2015; Zhou et al., 2020).”