Language learning using Machine Learning: a systematic review
Language learning using machine learning (ML) has become significant, employing techniques and algorithms with the ability to solve text, audio and image translation. The objective of the research is to determine the worldwide advances in language learning using ML in order to support and encourage...
Guardado en:
| Publicado en: | Apuntes Universitarios |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Artículo de revista |
| Lenguaje: | español |
| Publicado: |
Universidad Peruana Unión
2022
|
| Materias: | |
| Acceso en línea: | Ver en el sistema de origen |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Language learning using machine learning (ML) has become significant, employing techniques and algorithms with the ability to solve text, audio and image translation. The objective of the research is to determine the worldwide advances in language learning using ML in order to support and encourage researchers to undertake further experimental research in this area. The amount of research on language learning and ML requires a systematic review of the literature, limiting the review years between 2016 and 2021. The sources consulted are: Taylor & Francis, IEEE Xplore, ACM Digital Library, ScienceDirect, ProQuest, ARDI y ERIC. The studies initially found were 55237, after applying rigorous exclusion criteria 82 papers were obtained. The results of the review conclude that the most used technique for language learning using ML is support vector machine (SVM), followed by K-means (K-M) and the way the concepts have evolved tend to learning automation. The review gives us an approach to the trends in language learning with ML. In addition, the keywords that present more co-occurrence in the research are “machine learning”, “natural language processing” and “machine translation”. |
|---|---|
| ISSN: | 2304-0335 2225-7136 |