LSA潜在语义分析

低维的语义空间可以用于以下几个方面:

• 在低维语义空间可对文档进行比较，进而可用于文档聚类和文档分类。
• 在翻译好的文档上进行训练，可以发现不同语言的相似文档，可用于跨语言检索。
• 发现词与词之间的关系，可用于同义词、歧义词检测。.
• 通过查询映射到语义空间，可进行信息检索。
• 从语义的角度发现词语的相关性，可用于“选择题回答模型”（multi choice qustions answering model）。

（原文还说了一些其它方面，感觉不是很重要，不翻译了，放上原文）

Synonymy and polysemy are fundamental problems in natural language processing:

• Synonymy is the phenomenon where different words describe the same idea. Thus, a query in a search engine may fail to retrieve a relevant document that does not contain the words which appeared in the query. For example, a search for "doctors" may not return a document containing the word "physicians", even though the words have the same meaning.
• Polysemy is the phenomenon where the same word has multiple meanings. So a search may retrieve irrelevant documents containing the desired words in the wrong meaning. For example, a botanist and a computer scientist looking for the word "tree" probably desire different sets of documents.

Commercial applications[edit]

LSA has been used to assist in performing prior art searches for patents.^[5]

Applications in human memory[edit]

The use of Latent Semantic Analysis has been prevalent in the study of human memory, especially in areas of free recall and memory search. There is a positive correlation between the semantic similarity of two words (as measured by LSA) and the probability that the words would be recalled one after another in free recall tasks using study lists of random common nouns. They also noted that in these situations, the inter-response time between the similar words was much quicker than between dissimilar words. These findings are referred to as the Semantic Proximity Effect.^[6]

When participants made mistakes in recalling studied items, these mistakes tended to be items that were more semantically related to the desired item and found in a previously studied list. These prior-list intrusions, as they have come to be called, seem to compete with items on the current list for recall.^[7]

Another model, termed Word Association Spaces (WAS) is also used in memory studies by collecting free association data from a series of experiments and which includes measures of word relatedness for over 72,000 distinct word pairs.^[8]

算法局限性

LSA的一些缺点如下:

• 新生成的矩阵的解释性比较差.比如

{(car), (truck), (flower)} ? {(1.3452 * car + 0.2828 * truck), (flower)}

 (1.3452 * car + 0.2828 * truck) 可以解释成 "vehicle"。同时，也有如下的变换

{(car), (bottle), (flower)} ? {(1.3452 * car + 0.2828 * bottle), (flower)}

造成这种难以解释的结果是因为SVD只是一种数学变换，并无法对应成现实中的概念。

• LSA无法扑捉一词多以的现象。在原始词-向量矩阵中，每个文档的每个词只能有一个含义。比如同一篇文章中的“The Chair of Board"和"the chair maker"的chair会被认为一样。在语义空间中，含有一词多意现象的词其向量会呈现多个语义的平均。相应的，如果有其中一个含义出现的特别频繁，则语义向量会向其倾斜。

• LSA具有词袋模型的缺点，即在一篇文章，或者一个句子中忽略词语的先后顺序。

• LSA的概率模型假设文档和词的分布是服从联合正态分布的，但从观测数据来看是服从泊松分布的。因此LSA算法的一个改进PLSA使用了多项分布，其效果要好于LSA。