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Latent Semantic Indexing

The Wikipedia page on Latent Semantic Analysis, the mathematical model that conceptual analytics in Relativity uses, gives a good demonstration of how a matrix is formed with words on separate rows, and documents in separate columns, to find the distance between conceptually similar items.

Latent Semantic Indexing assumes that its initial calculation of the number of terms in each documents is too conservative. It needs to recalculate to account for the number of terms related to each document. The docuuments are analyzed to associate those which contain similar words.

The drawbacks of Latent Semantic Indexing are that:

  1. The math may justify terms being closely associated which have no real relationship, since the average of a word's meaning in the data set is used. But this discrepancy will be decreased where words in the document set are used consistently in a predominant context.

  2. In a Bag of Words model, the text is considered to be a bunch of words in no order. N-grams letter sequences are used to find relationships between terms.

  3. LSI uses a probabilistic model for the sample data that does not necessarily match up with the actual sample data.


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