Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
This paper is the first part of contextual predictability model investigation for Russian, it is focused on linguistic and psychology interpretation of models, features, metrics and sets of features. The aim of this paper is to identify the dependence of the implementation of contextual predictability procedures on the genre characteristics of the text (or text collection): scientific vs. fictional. We construct a model predicting text elements and designate its features for texts of different genres and domains. We analyze various methods for studying contextual predictability, carry out a computational experiment against scientific and fictional texts, and verify its results by the experiment with informants (cloze-tests) and word embeddings (word2vec CBOW model). As a result, text processing model is built. It is evaluated based on the selected contextual predictability features and experiments with informants.
Язык оригинала | английский |
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Название основной публикации | Mining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings |
Редакторы | P. B.R., Veena Thenkanidiyoor, Rajendra Prasath, Odelu Vanga |
Издатель | Springer Nature |
Глава | 11 |
Страницы | 104-119 |
Число страниц | 16 |
ISBN (печатное издание) | 9783030661861 |
DOI | |
Состояние | Опубликовано - 2020 |
Событие | 7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019 - Veling, Индия Продолжительность: 19 дек 2019 → 22 дек 2019 |
Название | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Том | 11987 LNAI |
ISSN (печатное издание) | 0302-9743 |
ISSN (электронное издание) | 1611-3349 |
конференция | 7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019 |
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Страна/Tерритория | Индия |
Город | Veling |
Период | 19/12/19 → 22/12/19 |
ID: 73342010