Fine-Grained Temporal Relation Extraction

Vashishtha, Siddharth; Durme, Benjamin Van; White, Aaron Steven

doi:10.18653/v1/p19-1280

Cited by 50 publications

(76 citation statements)

References 31 publications

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“…'GCL' (Meng and Rumshisky, 2018) is the global context layer model introduced in § 2.1. 'Fine-grained TRC ' Vashishtha et al (2019) which suggests the effectiveness of two main proposal. The 'SEC' model further outperforms 'Multi-BERT' by 3.6 gain of the majority category E2E, 1.0 gain of E2T and 0.7 gain of E2D, which indicates the impact of the global SEC RNN.…”

Section: Main Timebank-dense Resultsmentioning

confidence: 98%

“…Most existing temporal relation classification approaches focus on extracting various features from the textual sentence in the local pair-wise setting. Inspired by the success of neural networks in various NLP tasks, Cheng and Miyao (2017); Meng et al (2017); Vashishtha et al (2019); Han et al (2019b,a) propose a series of neural networks to achieve accuracy with less feature engineering. However, these neural models still drop in the pairwise setting.…”

Section: Temporal Relation Classificationmentioning

confidence: 99%

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Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Cheng¹,

Asahara²,

Kobayashi³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) 1 hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two transfer learning baselines on both the English and Japanese data.

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Section: Main Timebank-dense Resultsmentioning

confidence: 98%

Section: Temporal Relation Classificationmentioning

confidence: 99%

Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Cheng¹,

Asahara²,

Kobayashi³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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“…Recently, there have been interesting developments in annotating news texts with relative temporal information [9], [33], which are out of the scope of this work as we focus on extracting absolute timelines, which can be interpreted directly on the calendar.…”

Section: A Event Positionmentioning

confidence: 99%

Towards Extracting Absolute Event Timelines From English Clinical Reports

Leeuwenberg

Moens

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Temporal information extraction is a challenging but important area of automatic natural language understanding. Existing approaches annotate and extract various parts of the temporal information conveyed in language like relative event order, temporal expressions, or event durations. Most schemes focus primarily on annotation of temporally certain (often explicit) information, resulting in partial annotation, and underrepresentation of implicit information. In this paper, we propose an approach towards extraction of more complete (implicit and explicit) temporal information for all events, and obtain probabilistic absolute event timelines by modeling temporal uncertainty with information bounds. As a case study, we use our scheme to annotate a set of English clinical reports, and propose and evaluate a multi-regression model for predicting probabilistic absolute timelines, obtaining promising results.

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“…One problem with annotating preconditions in text is the large number of event mentions in each article, which means annotation of all possible event pairs is infeasible. The temporal community has struggled with this same dilemma (Chambers et al, 2014;Vashishtha et al, 2019).…”

Section: Preconditions Datasetmentioning

confidence: 99%

Modeling Preconditions in Text with a Crowd-sourced Dataset

Kwon

Koupaee

Singh³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Preconditions provide a form of logical connection between events that explains why some events occur together and information that is complementary to the more widely studied relations such as causation, temporal ordering, entailment, and discourse relations. Modeling preconditions in text has been hampered in part due to the lack of large scale labeled data grounded in text. This paper introduces PeKo, a crowd-sourced annotation of preconditions between event pairs in newswire, an order of magnitude larger than prior text annotations. To complement this new corpus, we also introduce two challenge tasks aimed at modeling preconditions: (i) Precondition Identification -a standard classification task defined over pairs of event mentions, and (ii) Precondition Generation -a generative task aimed at testing a more general ability to reason about a given event. Evaluation on both tasks shows that modeling preconditions is challenging even for today's large language models (LM). This suggests that precondition knowledge is not easily accessible in LM-derived representations alone. Our generation results show that fine-tuning an LM on PeKo yields better conditional relations than when trained on raw text or temporally-ordered corpora.

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Fine-Grained Temporal Relation Extraction

Cited by 50 publications

References 31 publications

Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Towards Extracting Absolute Event Timelines From English Clinical Reports

Modeling Preconditions in Text with a Crowd-sourced Dataset

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