Computational linguistics and discourse complexology: Paradigms and research methods

Abstract

The dramatic expansion of modern linguistic research and enhanced accuracy of linguistic analysis have become a reality due to the ability of artificial neural networks not only to learn and adapt, but also carry out automate linguistic analysis, select, modify and compare texts of various types and genres. The purpose of this article and the journal issue as a whole is to present modern areas of research in computational linguistics and linguistic complexology, as well as to define a solid rationale for the new interdisciplinary field, i.e. discourse complexology. The review of trends in computational linguistics focuses on the following aspects of research: applied problems and methods, computational linguistic resources, contribution of theoretical linguistics to computational linguistics, and the use of deep learning neural networks. The special issue also addresses the problem of objective and relative text complexity and its assessment. We focus on the two main approaches to linguistic complexity assessment: “parametric approach” and machine learning. The findings of the studies published in this special issue indicate a major contribution of computational linguistics to discourse complexology, including new algorithms developed to solve discourse complexology problems. The issue outlines the research areas of linguistic complexology and provides a framework to guide its further development including a design of a complexity matrix for texts of various types and genres, refining the list of complexity predictors, validating new complexity criteria, and expanding databases for natural language.

Topic Modeling for Text Structure Assessment: The case of Russian Academic Texts

“…For this purpose, researchers tend to use either collections of school textbooks, foreign language tests or collect corpora for specific research goals. As a rule of thumb in discourse complexology, suitability and 'complexity' of the dataset is to be evaluated by experts (Solovyev et al, 2022).…”

Aspectual pairs: Prefix vs. suffix way of formation

“…Обратим внимание на то, что второй из предложенных подходов -с вычислением векторной близости между словами на основе больших корпусов текстов -широко используется в компьютерной лингвистике (Solovyev et al 2022), но лишь в последнее время начал применяться для решения собственно лингвистических задач (Ryzhova & Paperno 2022, Shavrina 2021. Между тем он может оказаться перспективным в исследованиях, связанных с семантической близостью слов.…”

“…Her areas of interest are computational and theoretical linguistics. e-mail: vbayrasheva@gmail.com https://orcid.org/0000-0002-1729-034Х Сведения об авторах: Валерий Дмитриевич СОЛОВЬЕВ -доктор физико-математических наук, профессор, главный научный сотрудник НИЛ «Текстовая аналитика» Института филологии и межкультурной коммуникации Казанского федерального университета, Казань, Valery Solovyev et al 2022. Russian Journal of Linguistics 26 (4).…”

Verb database: Structure, clusters and options

“…Некоторые направления кажутся наиболее очевидными. Во-первых, это сближение с компьютерной лингвистикой, методы которой позволяют повышать точность расчетов лингвистического анализа благодаря способности искусственных нейронных сетей к обучению и возможности не только автоматизировать проводимый анализ, но и решать задачи отбора, модификации и сопоставления текстов различных типов и жанров (Solovyev, Solnyshkina & McNamara 2022, Sharoff 2022.…”