Temporal Relation Extraction
18 papers with code • 0 benchmarks • 3 datasets
Temporal relation extraction systems aim to identify and classify the temporal relation between a pair of entities provided in a text. For instance, in the sentence "Bob sent a message to Alice while she was leaving her birthday party." one can infer that the actions "sent" and "leaving" entails a temporal relation that can be described as "simultaneous".
Benchmarks
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Libraries
Use these libraries to find Temporal Relation Extraction models and implementations
2012 i2b2 Temporal Relations
Most implemented papers
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
vt-nlp/syntax-guided-graph-transformer
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Findings (NAACL) 2022
Extracting temporal relations (e. g., before, after, and simultaneous) among events is crucial to natural language understanding.
Extracting Event Temporal Relations via Hyperbolic Geometry
xingwei-warwick/hyper-event-temprel
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EMNLP 2021
Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.
Selecting Optimal Context Sentences for Event-Event Relation Extraction
hieumdt/SCS-EERE
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AAAI 2022
To achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event mentions/triggers in texts.
tieval: An Evaluation Framework for Temporal Information Extraction Systems
All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems.
End-to-End Temporal Relation Extraction in the Clinical Domain
jsaizant/ETEREX-REBEL
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Proceedings of the Text2Story'23 Workshop 2023
Temporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events.
Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models
In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction.
TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles
Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given document (those spanning across different paragraphs); and (3) the exclusion of events that are not centred on verbs.
