Machine Learning Advances and Applications on Natural Language Processing (NLP)
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The impressive achievements of recent state-of-the-art machine learning models are revolutionizing the area of text and language applications. On the one hand, popular large language models (LLMs) have demonstrated an enormous capability in automatic and reasonable text generation. On the other hand, Transformer-based models have drastically improved the performance of other, highly important natural language tools, including traditional sentiment analyzers, machine (auto)translators, and multimedia annotators. Furthermore, such natural language processing (NLP) techniques are heavily utilized...
The impressive achievements of recent state-of-the-art machine learning models are revolutionizing the area of text and language applications. On the one hand, popular large language models (LLMs) have demonstrated an enormous capability in automatic and reasonable text generation. On the other hand, Transformer-based models have drastically improved the performance of other, highly important natural language tools, including traditional sentiment analyzers, machine (auto)translators, and multimedia annotators. Furthermore, such natural language processing (NLP) techniques are heavily utilized in social networks, microblogs, eCommerce systems, and numerous other disciplines. This Reprint contains 10 high-quality, peer-reviewed original research papers in the field of natural language processing. These articles have been selected with the aim of providing the research community with insights that will open up new directions in this discipline. The authors of these papers have introduced new models, methods, and techniques related to large language models, sentiment analysis applications, effective text representations, text classification and text clustering.
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