Vector Space Model

How many documents contain a term and what are important terms each document has.

Vector space model. Both the documents and queries are represented using the bag of words model. Vector space model or term vector model is an algebraic model for representing text documents and any objects in general as vectors of identifiers such as index terms. Thus making a vector space model significant for unstructured data. The table shown is a feature vector where the numbers for each row have been normalized with the size of the image to make the row sum equal to one.

Vector space model is a statistical model for representing text information for information retrieval nlp text mining. It is used in information filtering information retrieval indexing and relevancy rankings. The model assumes that the relevance of a document to query is roughly equal to the document query similarity. A vector space model is an algebraic model involving two steps in first step we represent the text documents into vector of words and in second step we transform to numerical format so that we can apply any text mining techniques such as information retrieval information extraction information filtering etc.

These relatively simple models are especially good at representing phenomena that are not usually considered numerical and have been. Term frequency tf and inverse document frequency idf. Term frequency and inverse document frequency. Contains the following information.

Vector space models are representations built from vectors. But a document can mean any object you re trying to model. A vector space also called a linear space is a collection of objects called vectors which may be added together and multiplied scaled by numbers called scalars scalars are often taken to be real numbers but there are also vector spaces with scalar multiplication by complex numbers rational numbers or generally any field the operations of vector addition and scalar multiplication. In the vector space model vsm each document or query is a n dimensional vector where n is the number of distinct terms over all the documents and queries the i th index of a vector contains the score of the i th term for that vector.

Its first use was in the smart information retrieval system. Whether you explicitly understand this or not you ve used it in your machine learning projects. Similar vectors can be computed of the image texture shapes of objects and any other properties. The vector space model vsm is based on the notion of similarity.

Representing documents in vsm is called vectorizing text.