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Related Experiment Video

Updated: May 2, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction.

Zhonghua Wu^1,2, Wenzhong Yang^1,2, Meng Zhang^1,2

¹School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.

Entropy (Basel, Switzerland)

|March 28, 2025

Summary

This summary is machine-generated.

This study introduces a novel reinforcement learning framework for event temporal relation extraction, improving context word identification and generation accuracy. The approach enhances natural language processing models for better temporal understanding.

Keywords:

dependency path generative models multi-task learning policy gradient method reinforcement learning temporal relation extraction

Related Experiment Videos

Last Updated: May 2, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

Natural Language Processing
Artificial Intelligence
Computational Linguistics

Background:

Event temporal relation extraction is vital for understanding text.
Existing classification models lack contextual word output.
Generative models trained with maximum likelihood estimation face optimization challenges.

Purpose of the Study:

To develop a reinforcement learning-based generative framework for event temporal relation extraction.
To address limitations in existing classification and generative approaches.
To improve the accuracy and contextual understanding in temporal relation identification.

Main Methods:

Introduced a reinforcement learning-based generative framework.
Incorporated dependency path generation as an auxiliary task.
Utilized the REINFORCE algorithm with a novel reward function for optimization.
Proposed a baseline policy gradient algorithm to enhance training stability.

Main Results:

The proposed framework successfully outputs crucial contextual words.
Achieved competitive performance on the MATRES and TB-DENSE datasets.
Demonstrated improved accuracy in temporal prediction and generation quality.

Conclusions:

The reinforcement learning generative framework offers a promising solution for event temporal relation extraction.
The integration of dependency path generation enhances model performance.
The novel reward function and baseline policy gradient algorithm improve training efficiency and stability.