Advances in Natural Language Processing and Text Mining: 2nd Edition

Dear Colleagues,

Natural language processing (NLP) and text mining are two rapidly evolving fields with an increasing importance in both academic and industrial research areas. NLP focuses on the interaction between human language and computers, while text mining aims to extract useful insights and knowledge from unstructured textual data. Both fields are essential for handling the vast amounts of text data generated in today's world, which is crucial for various applications such as information retrieval, sentiment analysis, machine translation, and many others.

With the growing volume and complexity of textual data, new challenges and opportunities arise in NLP and text mining. Recent advancements in machine learning, deep learning, and artificial intelligence have led to significant improvements in these fields. However, there is still much room for innovation and research to tackle the existing challenges.

The aim of this Special Issue is to present the latest research and developments in NLP and text mining, including new methodologies, techniques, and applications. This Special Issue intends to bring together researchers, practitioners, and academics to showcase their work and share their knowledge and expertise in these fields. The scope of this Special Issue aligns with the broader scope of big data and cognitive computing, which focuses on exploring the intersection of big data, cognitive computing, and artificial intelligence. The subject matter of NLP and text mining directly relates to the journal’s scope, as these fields contribute significantly to the advancement of artificial intelligence and cognitive computing.

This Special Issue welcomes original research articles and reviews. Research areas may include (but are not limited to) the following:

• Natural language understanding: techniques and algorithms for understanding and analyzing natural language, including sentiment analysis, topic modeling, named entity recognition, and entity linking.
• Text mining and information retrieval: approaches for mining knowledge and insights from unstructured text data, including information retrieval, text classification, and clustering.
• Deep learning for NLP and text mining: deep learning-based techniques for natural language processing and text mining, including neural language models, sequence-to-sequence models, and attention-based models.
• Large language model pre-training: techniques for pre-training large language models, including BERT, GPT, and RoBERTa, and their applications in NLP and text mining tasks.
• Multimodal NLP: techniques for analyzing and understanding multimodal data, including text, images, and videos.
• Text generation: techniques for generating natural language text, including text summarization, question-answering systems, and text-to-speech systems.
• Applications of NLP and text mining: practical applications of NLP and text mining in various domains, including healthcare, finance, social media, and e-commerce.
• Explainable NLP and text mining: approaches for making NLP models more transparent and interpretable, including model visualization, attention mechanisms, and explainable AI.
• Low-resource NLP and text mining: techniques for NLP tasks in low-resource languages or domains, where training data are scarce, including transfer learning, domain adaptation, and few-shot learning.
• Multilingual NLP and text mining: techniques for processing and analyzing text data in multiple languages, including multilingual embeddings, cross-lingual transfer learning, and multilingual topic modeling.
• NLP and text mining for social good: applications of NLP and text mining for social good, including hate speech detection, cyberbullying prevention, and disaster response.

We look forward to receiving your contributions.

Dr. Zuchao Li
Prof. Dr. Min Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• natural language processing
• text mining
• deep learning
• large language models
• information retrieval
• entity linking
• relation extraction
• multimodal NLP
• low-resource NLP
• NLP and text mining for social good

• Advances in Natural Language Processing and Text Mining in Big Data and Cognitive Computing (21 articles)