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Documents and queries are represented as vectors. Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is tf-idf weighting (see the example below). The definition of term depends on the application. Typically terms are single words, keywords, or longer phrases. If words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary (the number of distinct words occurring in the corpus). Vector operations can be used to compare documents with queries.

License: Mozilla Public License 2.0

Python 99.29% TeX 0.71%
data-processing information-retrieval lemmatization nlp openpyxl python search-engine tf-idf tkinter tkinter-gui tkinter-python trump-speeches vector-space-model

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