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
These leaderboards are used to track progress in Temporal Relation Extraction
Libraries
Use these libraries to find Temporal Relation Extraction models and implementationsMost implemented papers
Are Transformers Effective for Time Series Forecasting?
Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.
TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations
We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise.
Effective Distant Supervision for Temporal Relation Extraction
A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
CATENA: CAusal and TEmporal relation extraction from NAtural language texts
The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.
Structured Learning for Temporal Relation Extraction from Clinical Records
We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR).
Word-Level Loss Extensions for Neural Temporal Relation Classification
In this work, we extend our classification model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model.
Deep Structured Neural Network for Event Temporal Relation Extraction
We propose a novel deep structured learning framework for event temporal relation extraction.
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction
Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding.
EventPlus: A Temporal Event Understanding Pipeline
We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.