Финансовый менеджмент Введение Управление оборотными средствами предприятия включает в себя управление объемами оборотных средств и управление источниками их покрытия. Обычно рассматриваются три стратегии управления оборотными средствами: агрессивная, консерватив-ная и умеренная. Необходимо отметить, что в экономической литературе существует некоторая несогласованность в использовании этих названий [1-3]. Авторы настоящей работы придерживаются точки зрения Джеймса К. Ван Хорна [4], который считает «консерватизм» залогом ликвидности и консервативной признает такую модель управления, которая

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Motivation This paper describes NEREL-BIO – an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL (Loukachevitch et al., 2021) by introducing domain-specific entity types. NEREL-BIO annotation scheme covers both general and biomedical domains making it suitable for domain transfer experiments. NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL. Nested named entities may cross entity boundaries to connect to shorter entities nested within longer entities, making them harder to detect. Results NEREL-BIO contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. Thus, NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL → NEREL-BIO) and cross-language (English → Russian) transfer. We experiment with both transformer-based sequence models and machine reading comprehension (MRC) models and report their results. Availability The dataset and annotation guidelines are freely available at https://github.com/nerel-ds/NEREL-BIO.

The paper studies machine reading comprehension model (MRC) (Li et al., 2020) in its application to extracting nested named entities (nested NER) in the RuNNE-2022 evaluation (Artemova et al., 2022). The model transforms named entity recognition tasks to a question-answering task. In this paper we compare several approaches to formulating ”questions” for the MRC model such as entity type names (keywords), entity type definitions, most frequent examples for the train set, combinations of definitions and examples. We found that using two most frequent examples from the training set is comparable in quality of nested NER with gathering qualitative definitions from different dictionaries, which is much more complicated. In the RuNNE evaluation, the MRC model obtained the best results among models without any manual work (rules or additional manual annotation of texts).