Evaluation of Vector Transformations for Russian Static and Contextualized Embeddings

The authors of Word2Vec claimed that their technology could solve the word analogy problem using the vector transformation in the introduced vector space. By default, the same is true for both static and contextualized models. However, the practice demonstrates that sometimes such an approach fails. In this paper, we investigate several static and contextualized models trained for the Russian language and find out the reasons of such inconsistency. We found out that words of different categories demonstrated different behavior in the semantic space. Contextualized models tend to find phonological and lexical analogies, while static models are better in finding relations among geographical proper names. In most cases, the average accuracy for contextualized models is better than for static ones. Our experiments have demonstrated that in some cases the length of the vectors could differ more than twice, while for some categories most of the vectors could be perpendicular to the vector connecting average beginning and ending points.

Evaluation of Vector Transformations for Russian Word2Vec and FastText Embeddings

Authors of Word2Vec claimed that their technology could solve the word analogy problem using the vector transformation in the introduced vector space. However, the practice demonstrates that it is not always true. In this paper, we investigate several Word2Vec and FastText model trained for the Russian language and find out reasons of such inconsistency. We found out that different types of words are demonstrating different behavior in the semantic space. FastText vectors are tending to find phonological analogies, while Word2Vec vectors are better in finding relations in geographical proper names. However, we found out that just four out of fifteen selected domains are demonstrating accuracy more that 0.8. We also draw a conclusion that in a common case, the task of word analogies could not be solved using a random word pair taken from two investigated categories. Our experiments have demonstrated that in some cases the length of the vectors could differ more than twice. Calculation of an average vector leads to a better solution here since it closer to more vectors.

Semantic Contents of the Theory of Intersubjective Management Name

The theme of the study is to identify the meaning of the terms that used in the name of the intersubjective management theory, which can be interpreted in different ways. At the stage of the formation of innovative approaches to management, multiple interpretations and discrepancies in understanding the basic principles of the theory can have a negative value. Therefore, the purpose of this article is to reduce the ambiguity of the interpretation of the terms used, starting with the name of the new approach itself. The applied theoretical and empirical methods include analysis, synthesis, comparison, generalization, formalization, induction, deduction, analogy, problem statement, and literature research. The obtained results consist of the fact that for the first time an analysis and comparison of the definitions of the basic concepts included in the name of the theory of intersubjective management and presented in the scientific literature and dictionaries are carried out. On the basis of existing concepts, new definitions are given and new meaning is presented, embedded in the name by the author of the theory and shared by followers. The stated concept will help to understand the meaning of key terms for researchers (for the further development of the theory) and actors (for practical application).