From the course: Applied AI for IT Operations (AIOps)

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Latent semantic analysis (LSA) and latent semantic indexing (LSI)

Latent semantic analysis (LSA) and latent semantic indexing (LSI) - Python Tutorial

From the course: Applied AI for IT Operations (AIOps)

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Latent semantic analysis (LSA) and latent semantic indexing (LSI)

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- [Instructor] In this chapter, I will review two of the major techniques used for building the self-service help desk. Machine learning algorithms work only with numeric data. They do not understand text. One of the most recent and popular techniques to convert text into its numeric representation is called Latent Semantic Analysis or LSA. It can use the vectorized representation of documents to analyze relationships and arrive at a similarity model. It builds an index using the latent semantic indexing, or LSI technique, which measures the relationships between terms in an unstructured collection of text. The index can then be used to find similar documents based on commonly occurring phrases between the documents. More background information about LSA can be found in this Wikipedia link. In the next video, let us review the data that we will use for building the document model.

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