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        <title>GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents</title>
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            <namePart type="given">Yvette</namePart>
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            <namePart type="given">Matthew</namePart>
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    <abstract>Graphs provide a natural, intuitive, and holistic means to capture relationships between different text elements in Natural Language Processing (NLP) such as words, sentences, and documents. Recent advancements in the field of Graph Machine Learning (GML) have led to the development of numerous models to process text for various natural language applications, including but not limited to short-text classification, document classification, and others.At the heart of GML models, specifically those based on Graph Neural Networks (GNNs), lies the message passing operation which has shown to be an essential component for strong empirical performance in NLP.However, the number of message passing steps (often known as the radius) is fixed for all the nodes in existing GML models for NLP.Fixing the radius poses a fundamental restriction as nodes exhibit diverse properties and varying amounts of informative local structures in the input graph.This paper presents GAINER, a novel framework called Graph mAchine learnIng with Node-spEcific Radius, aimed at graph-based NLP. We propose non-neural and novel neural approaches built on the core ideas of GAINER.Through rigorous experimentation, we demonstrate the efficacy of GAINER in various popular NLP tasks.</abstract>
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%0 Conference Proceedings
%T GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents
%A Yadati, Naganand
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F yadati-2024-gainer
%X Graphs provide a natural, intuitive, and holistic means to capture relationships between different text elements in Natural Language Processing (NLP) such as words, sentences, and documents. Recent advancements in the field of Graph Machine Learning (GML) have led to the development of numerous models to process text for various natural language applications, including but not limited to short-text classification, document classification, and others.At the heart of GML models, specifically those based on Graph Neural Networks (GNNs), lies the message passing operation which has shown to be an essential component for strong empirical performance in NLP.However, the number of message passing steps (often known as the radius) is fixed for all the nodes in existing GML models for NLP.Fixing the radius poses a fundamental restriction as nodes exhibit diverse properties and varying amounts of informative local structures in the input graph.This paper presents GAINER, a novel framework called Graph mAchine learnIng with Node-spEcific Radius, aimed at graph-based NLP. We propose non-neural and novel neural approaches built on the core ideas of GAINER.Through rigorous experimentation, we demonstrate the efficacy of GAINER in various popular NLP tasks.
%U https://aclanthology.org/2024.eacl-long.36
%P 609-626

Markdown (Informal)

[GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents](https://aclanthology.org/2024.eacl-long.36) (Yadati, EACL 2024)

• GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents (Yadati, EACL 2024)

ACL

• Naganand Yadati. 2024. GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 609–626, St. Julian’s, Malta. Association for Computational Linguistics.