CNN在句子分类中的卓越表现：深度学习与预训练词向量

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本文主要探讨了卷积神经网络（Convolutional Neural Networks, CNN）在句子分类任务中的应用。作者Yoon Kim来自纽约大学，其电子邮件地址yhk255@nyu.edu。文章的核心内容聚焦于将预训练的词向量与CNN相结合，用于提升句子级别的分类性能。 CNN在自然语言处理领域的研究中展现出强大的潜力，尤其是在诸如情感分析和问题分类等多任务场景下。文章首先指出，即使在没有大量超参数调优的情况下，一个简单的CNN模型也能在多个基准测试上取得优异的成绩。这表明，基础的架构和预训练的词向量已经足以提供相当有效的性能。作者进一步强调了学习任务特定的词向量对于提升性能的重要性。通过微调这些词向量，模型能够更好地适应特定任务的需求，从而实现更好的分类效果。此外，文章还提出了一种对CNN架构的简单修改，允许同时利用任务特异性和静态词向量，旨在进一步提高模型的灵活性和性能。这项研究结果显示，使用CNN进行句子分类，特别是与预训练词向量相结合，已经成为当前最先进的技术之一，至少在7个任务中的4个上超越了先前的最佳水平。这证实了深度学习方法在自然语言处理中的广泛应用和巨大潜力，特别是在处理文本数据时，CNN能够捕捉到局部和全局特征，从而显著提升模型的理解和预测能力。总结来说，"Convolutional Neural Networks for Sentence Classification"这篇论文展示了如何通过优化CNN结构和利用预训练词向量来有效地解决句子分类问题，为深度学习在自然语言处理领域的实际应用提供了有力的证据和实践指导。它不仅突显了CNN在文本理解中的关键作用，也为后续研究者探索更高效、更具针对性的模型设计提供了有价值的方向。

Convolutional Neural Networks for Sentence Classiﬁcation

Yoon Kim

New York University

yhk255@nyu.edu

Abstract

We report on a series of experiments with

convolutional neural networks (CNN)

trained on top of pre-trained word vec-

tors for sentence-level classiﬁcation tasks.

We show that a simple CNN with lit-

tle hyperparameter tuning and static vec-

tors achieves excellent results on multi-

ple benchmarks. Learning task-speciﬁc

vectors through ﬁne-tuning offers further

gains in performance. We additionally

propose a simple modiﬁcation to the ar-

chitecture to allow for the use of both

task-speciﬁc and static vectors. The CNN

models discussed herein improve upon the

state of the art on 4 out of 7 tasks, which

include sentiment analysis and question

classiﬁcation.

1 Introduction

Deep learning models have achieved remarkable

results in computer vision (Krizhevsky et al.,

2012) and speech recognition (Graves et al., 2013)

in recent years. Within natural language process-

ing, much of the work with deep learning meth-

ods has involved learning word vector representa-

tions through neural language models (Bengio et

al., 2003; Yih et al., 2011; Mikolov et al., 2013)

and performing composition over the learned word

vectors for classiﬁcation (Collobert et al., 2011).

Word vectors, wherein words are projected from a

sparse, 1-of-V encoding (here V is the vocabulary

size) onto a lower dimensional vector space via a

hidden layer, are essentially feature extractors that

encode semantic features of words in their dimen-

sions. In such dense representations, semantically

close words are likewise close—in euclidean or

cosine distance—in the lower dimensional vector

space.

Convolutional neural networks (CNN) utilize

layers with convolving ﬁlters that are applied to

local features (LeCun et al., 1998). Originally

invented for computer vision, CNN models have

subsequently been shown to be effective for NLP

and have achieved excellent results in semantic

parsing (Yih et al., 2014), search query retrieval

(Shen et al., 2014), sentence modeling (Kalch-

brenner et al., 2014), and other traditional NLP

tasks (Collobert et al., 2011).

In the present work, we train a simple CNN with

one layer of convolution on top of word vectors

obtained from an unsupervised neural language

model. These vectors were trained by Mikolov et

al. (2013) on 100 billion words of Google News,

and are publicly available.

We initially keep the

word vectors static and learn only the other param-

eters of the model. Despite little tuning of hyper-

parameters, this simple model achieves excellent

results on multiple benchmarks, suggesting that

the pre-trained vectors are ‘universal’ feature ex-

tractors that can be utilized for various classiﬁca-

tion tasks. Learning task-speciﬁc vectors through

ﬁne-tuning results in further improvements. We

ﬁnally describe a simple modiﬁcation to the archi-

tecture to allow for the use of both pre-trained and

task-speciﬁc vectors by having multiple channels.

Our work is philosophically similar to Razavian

et al. (2014) which showed that for image clas-

siﬁcation, feature extractors obtained from a pre-

trained deep learning model perform well on a va-

riety of tasks—including tasks that are very dif-

ferent from the original task for which the feature

extractors were trained.

2 Model

The model architecture, shown in ﬁgure 1, is a

slight variant of the CNN architecture of Collobert

et al. (2011). Let x

∈ R

be the k-dimensional

word vector corresponding to the i-th word in the

sentence. A sentence of length n (padded where

https://code.google.com/p/word2vec/

arXiv:1408.5882v2 [cs.CL] 3 Sep 2014

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CNN在句子分类中的卓越表现：深度学习与预训练词向量

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