| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560779 |
| ISSN: | 1405-5546 |
| Autores: | Abramov, Aleksei V1 Ivanov, Vladimir V2 Solovyev, Valery D1 |
| Instituciones: | 1Kazan Federal University, Instiute of Computational Mathematics and Information Technologies, Kazan. Rusia 2Innopolis University, Institute of Software Development and Software Engineering, Innopolis. Rusia |
| Año: | 2023 |
| Periodo: | Ene-Mar |
| Volumen: | 27 |
| Número: | 1 |
| Paginación: | 127-139 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | The task of identifying complex words within a context usually referred to as Complex Word Identification (CWI) or Lexical Complexity Prediction (LCP), is a vital component in Lexical Simplification pipelines. Correctness of complexity estimation depends on presented features, i.e. hand-crafted features, word embeddings, and presence of surrounding context, as well as on exploited rules or models, i.e. manually designed filtering, classic machine learning models, recurrent neural networks, and Transformer-based models. To our knowledge, the majority of existing works in CWI and LCP areas are devoted to investigating properties of English words and texts, accompanied by studies of German, Spanish, French and Hindu languages with little to no attention to Russian. In this paper, we present a study on lexical complexity estimation for the Russian language, by investigating the following topics: how well do morphological, semantic, and syntactic properties of a word represent its complexity; does a surrounding context significantly affect the accuracy of complexity estimation. We provide a brief description of the dataset of lexical complexity in context based on the Russian Synodal Bible and expand it by presenting a dataset of morphological, semantic, and syntactic features for annotated words. Additionally, we present linear regression and RuBERT models as baselines for lexical complexity estimation respectively. |
| Disciplinas: | Ciencias de la computación, Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Procesamiento de datos, Inteligencia artificial, Lingüística aplicada |
| Keyword: | Data processing, Artificial intelligence, Applied linguistics |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
