Abstract
Natural Language Processing (NLP) problems are among the hardest Machine Learning (ML) problems due to the complex nature of the human language. The introduction of word embeddings improved the performance of ML models on various NLP tasks as text classification, sentiment analysis, machine translation, etc. Word embeddings are real-valued vector representations of words in a specific vector space. Producing quality word embeddings that are then used as input to downstream NLP tasks is important in obtaining a good performance. To accomplish it, corpora of sufficient size is needed. Corpora may be formed in a multitude of ways, including text that was originally electronic, spoken language transcripts, optical character recognition, and synthetically producing text from the available dataset. The study provides the most recent bibliometric analysis on the topic of corpora generation for learning word vector embeddings. The analysis is based on the publication data from 2006 to 2022 retrieved from Scopus scientific database. A descriptive analysis method has been employed to obtain statistical characteristics of the publications in the research area. The systematic analysis results show the field’s evolution over time and highlight influential contributions to the field. It is believed that compiled bibliometric reviews could help researchers gain knowledge of the general state of the scientific knowledge, its descriptive features, patterns, and insights to design their studies systematically.
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This research is conducted within the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan under the grant number AP09260670 “Development of methods and algorithms for augmentation of input data for modifying vector embeddings of words.”
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Sagingaliyev, B., Aitakhunova, Z., Shaimerdenova, A., Akhmetov, I., Pak, A., Jaxylykova, A. (2022). A Bibliometric Review of Methods and Algorithms for Generating Corpora for Learning Vector Word Embeddings. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13613. Springer, Cham. https://doi.org/10.1007/978-3-031-19496-2_12
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