mlsanityData to 7 Oct 2026

A Global-Local Attention Mechanism for Relation Classification

arXiv 2407.01424 ↗First posted 1 Jul 2024cs.CL cs.IRcited by 1

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Abstract (arXiv)

Relation classification, a crucial component of relation extraction, involves identifying connections between two entities. Previous studies have predominantly focused on integrating the attention mechanism into relation classification at a global scale, overlooking the importance of the local context. To address this gap, this paper introduces a novel global-local attention mechanism for relation classification, which enhances global attention with a localized focus. Additionally, we propose innovative hard and soft localization mechanisms to identify potential keywords for local attention. By incorporating both hard and soft localization strategies, our approach offers a more nuanced and comprehensive understanding of the contextual cues that contribute to effective relation classification. Our experimental results on the SemEval-2010 Task 8 dataset highlight the superior performance of our method compared to previous attention-based approaches in relation classification.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 1 Jul 2024.

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10 of 25 references.

References · 10

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  4. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  5. 2023Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modelingcited by 56
  6. 2024SwitchTab: Switched Autoencoders Are Effective Tabular Learnerscited by 37
  7. 2015Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Pathcited by 35
  8. 2016Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentationcited by 26
  9. 2024Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Taskcited by 17
  10. 2024Deep Reinforcement Learning for Mobile Robot Path Planningcited by 5

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  1. 2025SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversationscited by 2