深度学习驱动的神经主题模型与监督扩展

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本文探讨了一种新型的神经主题模型及其有监督扩展在IT领域的研究价值。神经主题模型（Neural Topic Model, NTM）是一种结合了传统主题建模技术和深度学习优势的方法。它旨在解决传统主题模型如Latent Dirichlet Allocation (LDA)存在的问题，如对初始化敏感性和一元主题分布的局限性。首先，作者从神经网络的角度重新解释了标准的主题模型，强调了这种框架在统一处理单词和文档表示中的优势。在传统的概率框架下，主题模型通过将文本数据映射到潜在主题空间来捕捉文档的结构，但这种方法容易受到初始化设置的影响，导致模型收敛不稳定。神经网络的引入则提供了更加稳定的学习过程和更好的参数估计能力。作者提出的新型神经主题模型（NTM）在此基础上进行了创新。NTM通过设计高效的神经网络架构，使得单词和文档的表示能够自然地融合在一个统一的框架中。这种结合使得模型能够更好地理解和捕捉词语之间的复杂关系，同时减少了对初始参数的依赖，提高了模型的泛化能力和预测准确性。为进一步提升模型性能，文章还探讨了将监督学习元素融入NTM的方法，从而形成了有监督的神经主题模型（sNTM）。通过引入监督信号，sNTM能够利用外部标注信息来指导主题学习，增强主题的语义一致性，并在保持主题发现能力的同时，提高对特定任务的针对性，如情感分析、文本分类等。这篇研究论文的主要贡献在于提出了一种新颖的深度学习方法，将神经网络与主题模型相结合，有效地解决了传统主题模型的问题，并通过有监督扩展，使其在实际应用中展现出更强的性能和适应性。这不仅推动了主题模型的发展，也为其他领域的文本挖掘和信息检索任务提供了新的思考方向。对于IT专业人士和机器学习爱好者来说，理解并掌握这种新型模型的原理和应用将有助于他们在文本数据分析和信息处理中取得更精确的结果。

A Novel Neural Topic Model and Its Supervised Extension

Ziqiang Cao

Sujian Li

Yang Liu

Wenjie Li

Heng Ji

Key Laboratory of Computational Linguistics, Peking University, MOE, China

Computing Department, Hong Kong Polytechnic University, Hong Kong

Computer Science Department, Rensselaer Polytechnic Institute, USA

{ziqiangyeah, lisujian, pku7yang}@pku.edu.cn cswjli@comp.polyu.edu.hk jih@rpi.edu

Abstract

Topic modeling techniques have the beneﬁts of model-

ing words and documents uniformly under a probabilis-

tic framework. However, they also suffer from the limi-

tations of sensitivity to initialization and unigram topic

distribution, which can be remedied by deep learning

techniques. To explore the combination of topic mod-

eling and deep learning techniques, we ﬁrst explain the

standard topic model from the perspective of a neural

network. Based on this, we propose a novel neural topic

model (NTM) where the representation of words and

documents are efﬁciently and naturally combined into a

uniform framework. Extending from NTM, we can eas-

ily add a label layer and propose the supervised neu-

ral topic model (sNTM) to tackle supervised tasks. Ex-

periments show that our models are competitive in both

topic discovery and classiﬁcation/regression tasks.

Introduction

The real-world tasks of text categorization and document

retrieval rely critically on a good representation of words

and documents. So far, state-of-the-art techniques including

topic models (Blei, Ng, and Jordan 2003; Mcauliffe and Blei

2007; Wang, Blei, and Li 2009; Ramage et al. 2009) and

neural networks (Bengio et al. 2003; Hinton and Salakhutdi-

nov 2009; Larochelle and Lauly 2012) have shown remark-

able success in exploring semantic representations of words

and documents. Such models are usually embedded with la-

tent variables or topics, which serve the role of capturing the

efﬁcient low-dimensional representation of words and doc-

uments.

Topic modeling techniques, such as Latent Dirichlet Allo-

cation (LDA) (Blei, Ng, and Jordan 2003), have been widely

used for inferring a low dimensional representation that cap-

tures the latent semantics of words and documents. Each

topic is deﬁned as a distribution over words and each docu-

ment as a mixture distribution over topics. Thus, the seman-

tic representations of both words and documents are com-

bined into a uniﬁed framework which has a strict proba-

bilistic explanation. However, topic models also suffer from

certain limitations as follows. First, LDA-based models re-

quire prior distributions which are always difﬁcult to deﬁne.

 2015, Association for the Advancement of Artiﬁcial

Second, previous models rarely adopt n-grams beyond uni-

grams in document modeling due to the sparseness problem,

though n-grams are important to express text. Last, when

there is extra labeling information associated with docu-

ments, topic models have to do some task-speciﬁc transfor-

mation in order to make use of it (Mcauliffe and Blei 2007;

Wang, Blei, and Li 2009; Ramage et al. 2009), which may

be computationally costly.

Recently, deep learning techniques also make low di-

mensional representations (i.e., distributed representations)

of words (i.e., word embeddings) and documents (Bengio

et al. 2003; Mnih and Hinton 2007; Collobert and Weston

2008; Mikolov et al. 2013; Ranzato and Szummer 2008;

Hinton and Salakhutdinov 2009; Larochelle and Lauly 2012;

Srivastava, Salakhutdinov, and Hinton 2013) feasible. Word

embeddings provide a way of representing phrases (Mikolov

et al. 2013) and are easy to embed with supervised tasks

(Collobert et al. 2011). With layer-wise pre-training (Ben-

gio et al. 2007), neural networks are built to automatically

initialize their weight values. Yet, the main problem of deep

learning is that it is hard to give each dimension of the gener-

ated distributed representations a reasonable interpretation.

Based on the analysis above, we can see that current topic

modeling and deep learning techniques both exhibit their

strengths and defects in representing words and documents.

A question comes to our mind: Can these two kinds of tech-

niques be combined to represent words and documents si-

multaneously? This combination can on the one hand over-

come the computation complexity of topic models and on

the other hand provide a reasonable probabilistic explana-

tion of the hidden variables.

In our preliminary study we explain topic models from

the perspective of a neural network, starting from the fact

that the conditional probability of a word given a document

can be seen as the product of the probability of a word

given a topic (word-topic representation) and the probabil-

ity of a topic given the document (topic-document represen-

tation). At the same time, to solve the unigram topic dis-

tribution problem of a standard topic model, we make use

of the word embeddings available (Mikolov et al. 2013) to

represent n-grams. Based on the neural network explanation

and n-gram representation, we propose a novel neural topic

model (NTM) where two hidden layers are constructed to

efﬁciently acquire the n-gram topic and topic-document rep-

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

2210

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