大规模文档集合中主题模型的LDA推断方法

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"这篇文档是关于使用LDA（Latent Dirichlet Allocation）进行主题提取的研究，探讨在处理大规模、持续增长的文档集合时如何高效地进行主题模型推断。作者包括Limin Yao, David Mimno和Andrew McCallum，他们来自美国马萨诸塞大学阿默斯特分校计算机科学系。论文提出了不同的方法，包括基于Gibbs采样、变分推断以及一种受文本分类启发的新方法，以实现对新文档的主题分布推断，而无需重新训练模型。特别是，基于分类的推理方法通过一次矩阵乘法就能达到迭代推理方法类似的效果，显著提高了效率。此外，文中还介绍了Sp" 主题提取是自然语言处理中的一个关键任务，LDA是一种广泛应用的无监督机器学习算法，它能从大量文本数据中发现隐藏的主题结构。LDA假设每个文档都由多个主题混合而成，而每个主题又由一组特定的单词概率分布定义。通过LDA模型，我们可以将高维度的词汇数据映射到低维的主题空间，从而理解和分析文本数据。在处理大规模文本集合时，传统的LDA模型推断方法如Gibbs采样和变分推断，由于需要对所有文档进行多次迭代，计算成本较高。Gibbs采样是一种马尔科夫链蒙特卡洛方法，用于估计后验概率分布，尽管其能够获得精确的结果，但速度较慢。变分推断则通过优化一个近似后验分布来估计模型参数，虽然比Gibbs采样更快，但在某些情况下可能牺牲一定的准确性。论文中提出的基于文本分类的推理方法为解决这个问题提供了新的思路。这种方法借鉴了文本分类的快速计算特性，仅需一次矩阵乘法即可得到新文档的主题分布，极大地提高了效率，同时在效果上与迭代推理方法相当。这种创新使得在大型流式文档集合中实时地进行主题推断成为可能，无需每次有新数据到来时都重新训练整个模型，这对于大数据环境下的文本分析尤其重要。这篇研究不仅提供了关于LDA主题模型推断的新方法，还强调了在不断增长的数据集上进行有效推理的必要性。对于IT行业的专业人士来说，理解并掌握这些方法有助于优化文本分析流程，提高大数据处理的效率，尤其是在实时分析和预测领域。

Efﬁcient Methods for Topic Model Inference on Streaming

Document Collections

Limin Yao, David Mimno, and Andrew McCallum

Department of Computer Science

University of Massachusetts, Amherst

{lmyao, mimno, mccallum}@cs.umass.edu

ABSTRACT

Topic models provide a powerful tool for analyzing large

text collections by representing high dimensional data in a

low dimensional subspace. Fitting a topic model given a set

of training documents requires approximate inference tech-

niques that are computationally expensive. With today’s

large-scale, constantly expanding document collections, it is

useful to be able to infer topic distributions for new doc-

uments without retraining the model. In this paper, we

empirically evaluate the performance of several methods for

topic inference in previously unseen documents, including

methods based on Gibbs sampling, variational inference, and

a new method inspired by text classiﬁcation. The classiﬁcation-

based inference method produces results similar to iterative

inference methods, but requires only a single matrix multi-

plication. In addition to these inference methods, we present

SparseLDA, an algorithm and data structure for evaluat-

ing Gibbs sampling distributions. Empirical results indicate

that SparseLDA can be approximately 20 times faster than

traditional LDA and provide twice the speedup of previously

published fast sampling methods, while also using substan-

tially less memory.

Categories and Subject Descriptors

H.4 [Information Systems Applications]: Miscellaneous

General Terms

Experimentation, Performance, Design

Keywords

Topic modeling, inference

1. INTRODUCTION

Statistical topic model ing has emerged as a popular method

for analyzing large sets of categorical data in applications

from text mining to image analysis to bioinformatics. Topic

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for proﬁt or commercial advantage and that copies

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permission and/or a fee.

KDD’09, June 28–July 1, 2009, Paris, France.

models such as latent Dirichlet allocation (LDA) [3] have the

ability to identify interpretable low dimensional components

in very high dimensional data. R eprese nting documents as

topic distributions rather than bags of words reduces the ef-

fect of lexical variability while retaining the overall semantic

structure of the corpus.

Although there have recently been advances in fast infer-

ence for topic models, it remains computationally expensive.

Full topic model inference remains infeasible in two common

situations. First, data streams such as blog posts and news

articles are continually updated, and often require real-time

responses in computationally limited settings such as mobile

devices. In this case, although it may periodically be possi-

ble to retrain a model on a snapshot of the entire collection

using an expensive “oﬄine” computation, it is necessary to

be able to project new documents into a latent topic space

rapidly. Second, large scale collections such as information

retrieval corpora and digital libraries may be too big to pro-

cess eﬃciently. In this case, it would be useful to train a

model on a random sample of documents, and then project

the remaining documents into the latent topic space i nde-

pendently using a MapReduce-style process. In both cases

there is a need for accurate, eﬃcient methods to infer topic

distributions for documents outside the training corpus. We

refer to this task as “inference”, as distinct from “ﬁtting”

topic model parameters from training data.

This paper has two main contributions. First, we present

a new method for topic model infe rence in unseen documents

that is inspired by techniques from discriminative text clas-

siﬁcation. We evaluate the performance of this method and

several other methods for topic model inference in terms of

speed and accuracy relative to fully retraining a model. We

carried out experim ents on two datasets, NIPS and Pubmed.

In contrast to Banerjee and Basu [1], who evaluate diﬀerent

statistical models on streaming tex t data, we focus on a sin-

gle model (LDA) and compare diﬀerent inference methods

based on this model. Second, since many of the methods we

discuss rely on Gi bbs sampling to infer topic distributions,

we also present a simple method, SparseLDA, for eﬃcient

Gibbs sampling in topic models al ong with a data structure

that results i n very fast sampling performance with a small

memory footprint. SparseLDA is approximately 20 times

faster than highly optimized traditional LDA and twice the

speedup of previously published fast sampling methods [7].

2. BACKGROUND

A statistical topic model represents the words in docu-

ments in a collection W as mixtures of T “topics,” which

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