深度学习：卷积神经网络在文本分类中的应用探索

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本文档深入探讨了卷积神经网络（CNN）在文本分类领域的有效应用，特别是在利用文本数据的1D结构，即词序，以提高预测准确性。传统的文本分类方法通常依赖于低维词向量作为输入，但本文提出了一种创新的方法，即直接将高维度的文本数据馈送到CNN中，从而使得模型能够学习到小文本区域的嵌入，这在分类过程中起到了关键作用。作者们摒弃了常规的输入处理方式，直接将文本视为序列数据进行处理，而不是预先转换为词向量。他们引入了一种新的变体，即在卷积层中使用“词袋”转换，这种方法简单却富有创意，它能更好地捕捉文本中的局部特征，有助于增强模型的表达能力。此外，文中还探索了多层卷积层的结合，目的是进一步提升模型的准确性和复杂性处理能力。通过这种方式，CNN能够捕捉不同长度和上下文范围的模式，这对于理解和解析文本的语义至关重要。实验部分展示了这种基于CNN的文本分类方法相对于当前最先进的技术，其有效性得到了显著的证明。实验结果表明，直接使用CNN处理文本数据，并结合适当的结构设计，能够在保持简洁的同时，提升文本分类任务的性能，这为自然语言处理领域带来了新的思考方向。这篇论文不仅阐述了CNN在文本分类中的潜在优势，而且还提供了一套实践性的方法和策略，为研究者和开发者在实际应用中使用CNN进行文本分析和理解提供了有价值的技术参考。通过本文的研究，我们可以期待在未来的文本处理任务中看到更加高效和精准的CNN模型应用。

Effective Use of Word Order for Text Categorization

with Convolutional Neural Networks

Rie Johnson

RJ Research Consulting

Tarrytown, NY, USA

riejohnson@gmail.com

Tong Zhang

Baidu Inc., Beijing, China

Rutgers University, Piscataway, NJ, USA

tzhang@stat.rutgers.edu

Abstract

Convolutional neural network (CNN) is a neu-

ral network that can make use of the inter-

nal structure of data such as the 2D structure

of image data. This paper studies CNN on

text categorization to exploit the 1D structure

(namely, word order) of text data for accurate

prediction. Instead of using low-dimensional

word vectors as input as is often done, we

directly apply CNN to high-dimensional text

data, which leads to directly learning embed-

ding of small text regions for use in classiﬁ-

cation. In addition to a straightforward adap-

tation of CNN from image to text, a sim-

ple but new variation which employs bag-of-

word conversion in the convolution layer is

proposed. An extension to combine multiple

convolution layers is also explored for higher

accuracy. The experiments demonstrate the

effectiveness of our approach in comparison

with state-of-the-art methods.

1 Introduction

Text categorization is the task of automatically as-

signing pre-deﬁned categories to documents writ-

ten in natural languages. Several types of text cat-

egorization have been studied, each of which deals

with different types of documents and categories,

such as topic categorization to detect discussed top-

ics (e.g., sports, politics), spam detection (Sahami et

al., 1998), and sentiment classiﬁcation (Pang et al.,

2002; Pang and Lee, 2008; Maas et al., 2011) to de-

termine the sentiment typically in product or movie

reviews. A standard approach to text categorization

is to represent documents by bag-of-word vectors,

To appear in NAACL HLT 2015.

namely, vectors that indicate which words appear in

the documents but do not preserve word order, and

use classiﬁcation models such as SVM.

It has been noted that loss of word order caused

by bag-of-word vectors (bow vectors) is particularly

problematic on sentiment classiﬁcation. A simple

remedy is to use word bi-grams in addition to uni-

grams (Blitzer et al., 2007; Glorot et al., 2011; Wang

and Manning, 2012). However, use of word n-grams

with n > 1 on text categorization in general is not

always effective; e.g., on topic categorization, sim-

ply adding phrases or n-grams is not effective (see,

e.g., references in (Tan et al., 2002)).

To beneﬁt from word order on text categoriza-

tion, we take a different approach, which employs

convolutional neural networks (CNN) (LeCun et al.,

1986). CNN is a neural network that can make use

of the internal structure of data such as the 2D struc-

ture of image data through convolution layers, where

each computation unit responds to a small region of

input data (e.g., a small square of a large image).

We apply CNN to text categorization to make use of

the 1D structure (word order) of document data so

that each unit in the convolution layer responds to a

small region of a document (a sequence of words).

CNN has been very successful on image clas-

siﬁcation; see e.g., the winning solutions of Im-

ageNet Large Scale Visual Recognition Challenge

(Krizhevsky et al., 2012; Szegedy et al., 2014; Rus-

sakovsky et al., 2014).

On text, since the work on token-level applica-

tions (e.g., POS tagging) by Collobert et al. (2011),

CNN has been used in systems for entity search, sen-

tence modeling, word embedding learning, product

feature mining, and so on (Xu and Sarikaya, 2013;

Gao et al., 2014; Shen et al., 2014; Kalchbrenner et

arXiv:1412.1058v2 [cs.CL] 26 Mar 2015

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