What technique does a conceptual index use to discover concepts between documents?

A conceptual index leverages Latent Semantic Indexing (LSI) to identify concepts that may exist between documents. LSI is a technique used in natural language processing and information retrieval that helps in uncovering the relationships between words and the underlying concepts they represent. It analyzes patterns of word co-occurrences in a dataset to reveal associations and semantic similarities that may not be immediately obvious through mere keyword matching.

By utilizing LSI, a conceptual index is capable of improving the search and retrieval process by understanding the context in which terms appear, thus enabling it to connect documents that share similar ideas, themes, or notions. This capability makes LSI particularly powerful for tasks like clustering related documents or enhancing search results based on context rather than mere keyword frequency.

The other choices presented do not represent techniques that are typically associated with conceptual indexing in the same way LSI does. Support Vector Machines (SVM) are primarily used for classification tasks, while Conceptual Semantic Indexing (CSI) and Latent Vector Indexing (LVI) are not standard terms or widely recognized techniques in the field of document analysis or concept discovery.