Latent Semantic Indexing (LSI) is a technique used in natural language processing (NLP) to analyse and represent the meaning of a text.
LSI uses a mathematical model to identify patterns of similarity between words and documents, creating a vector representation of the text that reflects its semantic content.
This technique is commonly used in search engines and recommender systems to improve the accuracy of results and suggestions, as it can identify subtle semantic relationships between words and documents that are not evident in their literal form.
Machine learning is a branch of artificial intelligence (AI) that is based on making a system capable of learning from the information it receives.
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