.png){kind=icon}
返回
Temporal relation extraction with contrastive prototypical sampling
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2024.111410.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Temporal relation extraction aims to infer the temporal order of either two events in the document. Because of the nature of events in real life, severe imbalanced temporal relation classes exist in the temporal relation extraction task. Even though various methods have been proposed to improve the overall performance, the accuracy of these methods on minority temporal classes is limited. In this work, we present a contrastive prototypical learning architecture to address this problem, which explicitly models the spatial similarity between instances in the embedding space so that instances from minority classes can be distinguished from the large classes. To make it compatible with current temporal relation extraction settings, we propose a novel sampling memory queue-based method so that the architecture can be applied to a limited batch size scenario. We further design a context encoding layer that incorporates both contextualized information and linguistic features such as tense information and dependency. Our extensive experiments on TimeBank-Dense, TDDiscourse, and MATRES datasets demonstrate that our model can significantly improve the performance of minority relation classes and, therefore increase the overall learning ability.
Keyword:
Temporal relation extraction
Prototypical networks
Contrastive learning
Imbalanced data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Generation of hydroxyl radicals by urban suspended particulate air matter. The role of iron ions城市悬浮颗粒物产生羟基自由基。铁离子的作用:
PREAI
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Mutation effect estimation on protein-protein interactions using deep contextualized representation learning基于深度情境化表示学习的蛋白质-蛋白质相互作用突变效应估计OAAI
OAAI分享收藏Virtual prompt pre-training for prototype-based few-shot relation extraction基于原型的少镜头关系提取的虚拟提示预训练OAAI
OAAI分享收藏 没有更多内容
没有更多内容