Vector Space Models

Vector space models (VSMs) are a mathematical framework for representing text as vectors of numbers. They are used in a variety of natural language processing (NLP) tasks, such as text classification, text clustering, and information retrieval. VSMs are based on the idea that the meaning of a text can be represented by the words that it contains, and that the relationships between words can be captured by the distances between their vectors.

Vector Space Models

A vector space model is a mathematical model that represents text as vectors of numbers. Each vector in the vector space represents a document, and the components of the vector correspond to the words in the document. The value of each component indicates the importance of the corresponding word in the document. For example, a document that contains the word "the" many times will have a high value for the component corresponding to the word "the" in its vector.

The vectors in a vector space can be used to compute the similarity between documents. The cosine similarity between two vectors is a measure of the angle between them. The closer the angle between two vectors, the more similar the documents they represent. Cosine similarity can be used to find similar documents for a given query, or to cluster documents into groups of similar documents.

Types of Vector Space Models