What does Latent Semantic Indexing primarily focus on?

Latent Semantic Indexing (LSI) primarily focuses on the statistical correlation between terms within documents. This technique employs mathematical methods to identify patterns in the relationships and contexts of words, allowing for a deeper understanding of the meaning behind the terms used in a body of text. By examining the co-occurrence of words in a corpus, LSI can uncover latent structures in the data, which helps in improving information retrieval by associating similar concepts, even if the exact terms differ.

While the frequency of specific words in documents is important for text analysis, LSI goes beyond mere counts and statistics to analyze how words relate to each other in a more nuanced way. Additionally, categorization of documents by topic is a potential outcome of using LSI, but it is not the central focus of the methodology. Finally, the physical placement of documents in storage is not relevant to LSI, as the technique is concerned with semantic relationships rather than logistical organization. Hence, the focus on statistical correlations makes it the correct answer.