title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐ²ÑÐ·ÑÐ²Ð°Ð½Ð¸Ñ Ð¸Ð¼ÐµÐ½Ð¾Ð²Ð°Ð½Ð½ÑÑ ÑÑÑÐ½Ð¾ÑÑÐµÐ¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÐºÐ¾Ð½ÑÐµÐºÑÑÐ° ÑÐ¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ Ð¸ Ð¼Ð¾ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: An algorithm for linking named entities usingthe context of usage and morphological features
Creators: name: ÐÐ³Ð°ÐµÐ², ÐÑÐ»Ð°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
ÑÐ²ÑÂ­Ð·ÑÐ²Ð°Ð½Ð¸Ðµ Ð¸Ð¼ÐµÐ½Ð¾Ð²Ð°Ð½Ð½ÑÑ ÑÑÑÐ½Ð¾ÑÑÐµÐ¹
ÑÐ°ÑÑÐµÑÐµÑÐ½Ð°Ñ ÑÐ°Ð·Ð¼ÐµÑÐºÐ°
ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¼Ð¾ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸
natural language processing
named enitites linking
part of speech tagging
semantic analysis
morphological features

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description: The topic of the master's thesis: âAn algorithm for linking named entities using the context of usage and morphological features.âThis work is devoted to the problem of natural language processing called linking named entities, in particular, the development of a vector algorithm for linking named entities, which uses information about the relationship between words and is effective in the context of homonymy contexts of the use of named entities.The proposed algorithm is notable for adjusting the weights of the vector representation model according to the developed rules, compliance with which indicates particular importance. A system was developed that implements the proposed algorithm, as well as alternative known algorithms for comparing the results. Experiments were carried out on the body of news articles, the f1 measure was used to assess the quality.As a result, a conclusion was made about the effectiveness of using information about the relationships between words to improve the quality of linking named entities. The f1 measure for the proposed algorithm was 0.71 versus 0.67 and 0.64 for the known methods.