Shandong Yuan;Zili Zou;Han Zhou;Yun Ren;Jianping Wu;Kai Yan
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引用次数: 0
Abstract
Sentence classification constitutes a fundamental task in natural language processing. Convolutional Neural Networks (CNNs) have gained prominence in this domain due to their capacity to extract n-gram features through parallel convolutional filters, effectively capturing local lexical correlations. However, due to the constrained receptive field of convolutional operations, conventional CNNs exhibit limitations in modeling long-range contextual dependencies. To address this, attention mechanisms âĂŞ which enable global contextual modeling and keyword saliency detection âĂŞ have been integrated with CNN architectures to enhance classification performance. Diverging from conventional approaches that emphasize lexical-level attention, this study introduces a novel Squeeze-and-Excitation Convolutional Neural Network (SECNN) that implements channel-wise attention on CNN feature maps. Specifically, SECNN aggregates multi-scale convolutional features as distinct semantic channels and employs Squeeze-and-Excitation (SE) blocks to learn channel-wise attention weights, thereby enabling dynamic feature recalibration based on inter-channel dependencies. Across MR, IMDb, AGNews and DBpedia benchmark datasets, the proposed model achieves marginal yet consistent improvements (0.2% F1 on MR; 0.1% on DBpedia) over baseline methods, suggesting statistically advantages in two of four evaluated tasks.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.