  The task of determining the similarity of text documents has received
considerable attention in many areas such as Information Retrieval, Text
Mining, Natural Language Processing (NLP) and Computational Linguistics.
Transferring data to numeric vectors is a complex task where algorithms such as
tokenization, stopword filtering, stemming, and weighting of terms are used.
The term frequency - inverse document frequency (TF-IDF) is the most widely
used term weighting method to facilitate the search for relevant documents. To
improve the weighting of terms, a large number of TF-IDF extensions are made.
In this paper, another extension of the TF-IDF method is proposed where
synonyms are taken into account. The effectiveness of the method is confirmed
by experiments on functions such as Cosine, Dice and Jaccard to measure the
similarity of text documents for the Kazakh language.
