DOI

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 оригиналаанглийский
Название основной публикацииMining Intelligence and Knowledge Exploration - 7th International Conference, MIKE 2019, Proceedings
РедакторыP. B.R., Veena Thenkanidiyoor, Rajendra Prasath, Odelu Vanga
ИздательSpringer Nature
Глава11
Страницы104-119
Число страниц16
ISBN (печатное издание)9783030661861
DOI
СостояниеОпубликовано - 2020
Событие7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019 - Veling, Индия
Продолжительность: 19 дек 201922 дек 2019

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том11987 LNAI
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

конференция

конференция7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019
Страна/TерриторияИндия
ГородVeling
Период19/12/1922/12/19

    Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)

ID: 73342010