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Patent-based import substitution analysis with Additively Regularized Topic Models
CEUR Workshop Proceedings. 2021. No. 2830. P. 16–27.
Милкова М. А.
The rapid accumulation of textual data forces the use of various methods to present the structure of available information. One of these methods is topic modeling. We apply Additively Regularized Topic Models (ARTM) for analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1500 products and technologies for the proposed import substitution. The use of patent search based on ARTM allows to search immediately by the blocks of a priori information - terms of industrial plans for import substitution, and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to obtain more detailed information about which groups of products and technologies have been patented. It is important that topic modeling also solves the problem of synonymy and homonymy of words
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Topic modeling is a widely used instrument for the analysis of large text collections.
In the last few years, neural topic models and models with word embeddings have
been proposed to increase the quality of topic solutions. However, these models
were not extensively tested in terms of stability and interpretability. Moreover, the
question of selecting the number of topics ...
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