Vector space model is used for the representation of meanings in the form of vectors of lexical items. A vector space model can easily be modeled using linear algebra. So the similarity between vectors can be computed easily.

Vector size is used to represent the size of the vector being used that represents a particular context. The window-based method and dependency-based method are used for the modeling context. In the window-based method, the context is determined using the occurrence of words within the window of a particular size. In a dependency-based method, the context is determined when there is an occurrence of a word in a particular syntactic relation with the corresponding target word. Features or contextual words are stemmed and lemmatized. Similarity metrics can be used to compute the similarity between the two vectors.

Let's see the following list of similarity metrics:

Weighting scheme is another term that is very important as it provides information that the given context is more related to the target word.

Let's see the list of weighting schemes that can be considered: