Search results for “SAGE”

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High-Performance Computing Media and Communication

3 results

Hierarchical Interpretable Topical Embeddings for Exploratory Search and Real-Time Document Tracking

2020Journal articleAnastasia Ianina, Konstantin Vorontsov

International Journal of Embedded and Real-Time Communication Systems

Real-time monitoring of scientific papers and technological news requires fast processing of complicated search demands motivated by thematically relevant information acquisition. For this case, the authors develop an exploratory search engine based on probabilistic hierarchical topic modeling. Topic model gives a low dimensional sparse interpretable vector representation (topical embedding) of a text, which is used for ranking documents by their similarity to the query. They explore several ways of comparing topical vectors including searching with thematically homogeneous text segments. Topical hierarchies are built using the regularized EM-algorithm from BigARTM project. The topic-based search achieves better precision and recall than other approaches (TF-IDF, fastText, LSTM, BERT) and even human assessors who spend up to an hour to complete the same search task. They also discover that blending hierarchical topic vectors with neural pretrained embeddings is a promising way of enriching both models that helps to get precision and recall higher than 90

Improving the Quality of Machine Translation Using the Reverse Model

2022Journal articleN. A. Skachkov, K. V. Vorontsov

Automation and Remote Control

Machine translation is a natural language text processing task that aims to automatically translate input text from one language into another language. The currently known machine translation models show a fairly high quality of translation between large languages, but for smaller language areas, represented by less data, the problem is still not solved. Different methods are used to deal with various errors in automatic translation systems. This paper discusses approaches that use translation models of reverse language directions and improve consistency between translations of the same text using direct and reverse translation models. The paper presents a general theoretical justification for such methods in terms of solving the likelihood maximization problem and also proposes a method for stable training of modern models using cyclic translations.

Combinatorial Substantiation of Learning Algorithms

2009Journal articleKonstantin Vorontsov

Abstract—Combinatorial cross-validation functionals that characterize the generalization performance of learning algorithms are considered. Upper bounds are derived that are tighter than those in the Vapnik–Chervonenkis statistical theory. The initial data set is not assumed to be independent, identically distributed, or even random. The effect of localization of an algorithm family is described, and the concept of a local growth function is introduced. The basic principles of statistical theory are revised by using the combinatorial approach. The basic causes of complexity bound overestimation are analyzed. Keywords: computational learning theory, learning method, VC-dimension, local growth function, local effective VC-dimension. In learning theory, the generalization performance of a learning algorithm is characterized by the probability of an error. Unfortunately, this hypothetical quantity cannot be calculated or sometimes even satisfactorily evaluated, for example, in the case of small data sets. At the same time, in practice, any learning system deals only with finite data sets, both training and testing. Therefore, it is reasonable to characterize the generalization performance of algorithms with respect to finite data sets. Learning performance is empirically quantified by using independent testing sets, bootstrap, or cross-validation [1]. It is shown in this paper that upper bounds for cross-validation performance functionals can be derived without resorting