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New method uses graph neural networks for scientific literature representation

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel method for learning semantic representations of scientific literature using adaptive features and graph neural networks. The approach considers scientific literature features both globally and locally, employing a graph attention mechanism to weigh and aggregate document features based on citation relationships. By comparing mutual information between local and global semantic representations, the method aims to improve the learning of semantic representations, showing competitive results in scientific literature classification. AI

IMPACT This method could improve the organization and retrieval of scientific knowledge by enhancing how research papers are understood and categorized.

RANK_REASON The item is an academic paper detailing a new method for semantic representation learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method uses graph neural networks for scientific literature representation

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The item is an academic paper detailing a new method for semantic representation learning. [lever_c_demoted from research: ic=1 ai=1.0]
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108 days old
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hongrui Gao, Yawen Li, Meiyu Liang, Zeli Guan, Zhe Xue ·

    Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network

    arXiv:2311.00296v2 Announce Type: replace Abstract: Because most scientific literature data are unlabeled, semantic representation learning based on unsupervised graphs has become crucial. To enrich scientific-literature features, this paper proposes a semantic representation lea…

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