A new Natural Language Processing System for Multilingual and Low-Resource Languages

Natural Language Processing (NLP) is a commonly used branch of artificial intelligence that deals with human-computer interaction through natural language. This technology enables machines to understand, interpret, and produce human language, and with its help, intelligent systems such as voice assistants, automatic translators, chatbots, and text analysis tools have been developed. Combining linguistic knowledge and machine learning, NLP attempts to accurately analyze the relationship between words, sentences, and meanings and provide intelligent responses. While significant advancements have been made for high-resource languages, multilingual and low-resource languages remain underrepresented. This study explores the challenges and opportunities in Natural Language Processing (NLP) for multilingual and low-resource languages. We discuss the unique linguistic features, data scarcity issues, and the socio-cultural factors influencing the development of NLP tools for these languages. Furthermore, we highlight existing methodologies, such as transfer learning and data augmentation, that can enhance performance in low-resource scenarios. Through encouraging teamwork and innovative approaches, we strive to bridge the gap in NLP resources and technologies, ensuring inclusivity and accessibility for speakers of all languages. This study highlights the singnificance of developing robust NLP solutions that cater to the diverse linguistic landscape of our global society.

کلیدواژه ها:

Natural Language Processing ، Natural Language ، processing system ، Multilingual and Low-Resource ، French