The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora.

We propose a new technique for three-dimensional (3-D) imaging in arbitrary spectral intervals. It is based on a simultaneous diffraction of two divergent stereoscopic light beams on a single acoustic wave propagating in a uniaxial birefringent crystal. We discuss in detail this configuration of acousto-optic (AO) interaction, derive basic relations, and experimentally demonstrate the applicability of the proposed approach to 3-D spectral imaging. A stereo-imager of this type may be produced as an ultra-compact embeddable optical element, which is promising for many imaging applications.

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In this paper, we present a novel approach to spectral stereoscopic imaging. It is based on simultaneous spectral filtration of two light beams with a tunable acousto-optical filter (AOTF) of original design. It does not require large crystals and complicated optical relay systems, because two beams diffract in the same volume of the crystal medium but at different angles. We show that this geometry can be composed of a common-type AO cell and two triangular prisms of the same material. We derive equations, which specify the prism angles ensuring the necessary orientation of beams trajectories inside the crystal medium as well as parallel propagation of input and output beams. Some angles were additionally optimized for aberrations minimization by means of ray-tracing simulation. Experimental testing demonstrates rather high quality of spectral images, which is necessary for stereoscopic reconstruction procedure. The proposed approach makes possible development of spectral stereo-imaging components based on different types of previously developed AOTFs.

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