# with the same dimensionality as all the other vectors
## 6 Discussion and Conclusions  
The result of our work is a summary of text vectorization methods in the context of the obtained classification performance for two word-based methods (Continuous Bag of Words, Skip-gram) in two variants of feature extraction (by averaging and by the tf-idf method) and for one method based on the whole document (Distributed Memory Paragraph Vectors). This summary has been prepared for two very simple classification methods (k-nearest neighbor method and Naive Bayesian Classifier). These results give grounds to consider the choice of the text vectorization method as a factor influencing the classification performance. The obtained results can easily be considered reliable. All experiments were performed on a relatively large dataset (more than 12 thousand texts with an average length of about 70 words). This means that the size of the training set had no negative impact on the performance of the vectorization methods. In addition, the variety of categories for which it was possible to perform classification within a single text type (web news content) allowed for a good description of the relationship between the vectorization methods and classification performance. It is also worth emphasizing the fact that the course of the effectiveness function in successive epochs is independent of the classification method. As was already mentioned, this independence allows the choice of the method of text vectorization to be considered as an important factor determining the accuracy of the classification. Going further, it allows us to hypothesize that the creation of a text vectorization method combining the advantages of currently known methods or types of methods could further increase the \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Effectiveness**[17] & **Effectiveness** \\ \hline CBOW & 0.941 & 0.815 \\ doc2 vec & 0.939 & 0.310 \\ tf-idf & 0.931 & 0.842 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of classification performance results for CBOW, doc2 vec and tf-idf methods with the work [17]. classification efficiency. Which, as a consequence, would mean an increase in the efficiency and accuracy of the processing or understanding of texts by machines. The combination of these issues, together with methods that analyze speech and create their transcriptions, offers great opportunities for the development of AI. Worth noting is the relationship between learning speed and the type of text vectorization method. Word-based methods are slower than those based on vectorization of the entire text. At the same time, both types of vectorization methods produce similar classification performance results. It is worth considering a combination of word-based methods with simultaneous global context (relative to all words in the language) and whole-text-based methods with local context (only the texts on which the vectorization methods have been trained). This combination could allow for even better results while optimizing the number of learning epochs. Research is already underway on how text vectors should be created to best allow them to be represented. One example of a new approach to text vectorization is the use of an attention mechanism in the learning process [22].