latent语义分析技术的引入

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_latent Semantic Analysis_ Latent Semantic Analysis（LSA），也称为隐语义分析，是一种基于统计计算的文本挖掘技术，用于提取和表示单词的语境性意义。LSA 的主要思想是，通过对大量文本语料库的统计计算，捕捉单词在不同语境中的出现情况，从而揭示单词之间的相似性和相关性。 LSA 的提出者 Thomas K. Landauer、Peter W. Foltz 和 Darrell Laham 等人在他们的论文中指出，LSA 可以用于文本挖掘、信息检索和自然语言处理等领域。LSA 的优点是，能够自动地捕捉文本中的隐含语义关系，从而提高文本分析和检索的准确性。 LSA 的基本原理是，通过对文本语料库的统计计算，计算单词在不同语境中的出现频率和相互关系，从而构建单词之间的相似性矩阵。然后，通过矩阵分解和降维技术，提取单词的隐含语义特征，最后，通过计算单词之间的相似性度量，揭示单词之间的语义关系。 LSA 的应用非常广泛，包括文本分类、文本 clustering、信息检索、自然语言处理等领域。在文本分类中，LSA 可以用于自动地分类文本，提高文本分类的准确性。在文本 clustering 中，LSA 可以用于自动地将文本聚类，揭示文本之间的相似性关系。在信息检索中，LSA 可以用于提高检索结果的相关性和准确性。在自然语言处理中，LSA 可以用于语言模型的构建和改进。 LSA 的优点是，能够自动地捕捉文本中的隐含语义关系，提高文本分析和检索的准确性。但是，LSA 也存在一些缺点，例如，需要大量的文本语料库，计算复杂度高，需要高性能的计算机系统等。 LSA 是一种功能强大且广泛应用的文本挖掘技术，能够自动地捕捉文本中的隐含语义关系，提高文本分析和检索的准确性。

 

knowledge.) Thus, we propose to researchers in discourse processing not only that they

use LSA to expedite their investigations, but that they join in the project of testing,

developing and exploring its fundamental theoretical implications and limits.

What is LSA?

LSA is a fully automatic mathematical/statistical technique for extracting and inferring

relations of expected contextual usage of words in passages of discourse. It is not a

traditional natural language processing or artificial intelligence program; it uses no humanly

constructed dictionaries, knowledge bases, semantic networks, grammars, syntactic

parsers, or morphologies, or the like, and takes as its input only raw text parsed into words

defined as unique character strings and separated into meaningful passages or samples such

as sentences or paragraphs.

The first step is to represent the text as a matrix in which each row stands for a

unique word and each column stands for a text passage or other context. Each cell contains

the frequency with which the word of its row appears in the passage denoted by its

column. Next, the cell entries are subjected to a preliminary transformation, whose details

we will describe later, in which each cell frequency is weighted by a function that expresses

both the word’s importance in the particular passage and the degree to which the word type

carries information in the domain of discourse in general.

Next, LSA applies singular value decomposition (SVD) to the matrix. This is a

form of factor analysis, or more properly the mathematical generalization of which factor

analysis is a special case. In SVD, a rectangular matrix is decomposed into the product of

three other matrices. One component matrix describes the original row entities as vectors of

derived orthogonal factor values, another describes the original column entities in the same

way, and the third is a diagonal matrix containing scaling values such that when the three

components are matrix-multiplied, the original matrix is reconstructed. There is a

mathematical proof that any matrix can be so decomposed perfectly, using no more factors

 

than the smallest dimension of the original matrix. When fewer than the necessary number

of factors are used, the reconstructed matrix is a least-squares best fit. One can reduce the

dimensionality of the solution simply by deleting coefficients in the diagonal matrix,

ordinarily starting with the smallest. (In practice, for computational reasons, for very large

corpora only a limited number of dimensions—currently a few thousand— can be

constructed.)

Here is a small example that gives the flavor of the analysis and demonstrates what

the technique accomplishes. This example uses as text passages the titles of nine technical

memoranda, five about human computer interaction (HCI), and four about mathematical

graph theory, topics that are conceptually rather disjoint. Thus the original matrix has nine

columns, and we have given it 12 rows, each corresponding to a content word used in at

least two of the titles. The titles, with the extracted terms italicized, and the corresponding

word-by-document matrix is shown in Figure 1.



We will discuss the highlighted parts

of the tables in due course.

The linear decomposition is shown next (Figure 2); except for rounding errors, its

multiplication perfectly reconstructs the original as illustrated.

Next we show a reconstruction based on just two dimensions (Figure 3) that

approximates the original matrix. This uses vector elements only from the first two,

shaded, columns of the three matrices shown in the previous figure (which is equivalent to

setting all but the highest two values in S to zero).

Each value in this new representation has been computed as a linear combination of

values on the two retained dimensions, which in turn were computed as linear

combinations of the original cell values. Note, therefore, that if we were to change the entry

in any one cell of the original, the values in the reconstruction with reduced dimensions





This example has been used in several previous publications (e.g. Deerwester et al., 1990;

Landauer & Dumais, in press).

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latent语义分析技术的引入

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