Natural Language Processing Problems In Azerbaijani Language

Abstract

Natural language processing (nlp) has made significant progress in recent years, allowing machines to understand and process human language to a great extent. However, this progress has not been evenly distributed, with some languages having fewer resources and smaller communities facing significant challenges in nlp. Azerbaijani is one such language that faces many challenges in nlp due to its complex morphology, limited resources, and lack of standardization. This paper provides an overview of the major nlp problems in azerbaijani, including text preprocessing, named entity recognition, text classification, language modeling, and machine translation. We review the existing literature and the latest techniques used to address these challenges, including rule-based and machine learning-based approaches. We also highlight the potential solutions and future directions that can further enhance the performance of nlp systems in azerbaijani language. The solutions include developing new resources such as annotated corpora, improving machine learning models, and exploring deep learning techniques. We conclude that further research and development are required to overcome the challenges in nlp for azerbaijani language and to promote its use in various applications, such as information retrieval, sentiment analysis, and machine translation.