Convolutional Neural Networks for Sentence Classification

作者： Yoon Kim (2014)

arXiv： 1408.5882

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架构

TLDR（中文）

用 CNN + 预训练词向量做文本分类，证明"预训练 embedding + 简单架构"能打过手工特征工程，是预训练范式渗入 NLP 的早期标志。

TLDR (English)

Uses CNN with pre-trained word vectors for text classification, proving "pre-trained embedding + simple architecture" beats hand-crafted features. An early sign of pre-training paradigm entering NLP.

出现在这些文章里

• Embeddings：把离散符号放进连续空间
• Embeddings: Putting Discrete Symbols into Continuous Space

同被引用

这些论文与本文出现在同一篇文章中

• ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators Kevin Clark et al. (2020)
• Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov et al. (2013)
• Efficient Estimation of Word Representations in Vector Space Tomas Mikolov et al. (2013)
• GloVe: Global Vectors for Word Representation Jeffrey Pennington et al. (2014)

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