Reprint

Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications

Edited by
November 2022
304 pages
  • ISBN978-3-0365-5579-9 (Hardback)
  • ISBN978-3-0365-5580-5 (PDF)

This book is a reprint of the Special Issue Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
Summary

Natural language processing (NLP) is one of the most important technologies in use today, especially due to the large and growing amount of online text, which needs to be understood in order to fully ascertain its enormous value. During the last decade, the application of machine learning techniques has led to achievement of higher accuracy in many types of NLP applications. Although numerous machine learning models have been developed for NLP applications, deep learning approaches have recently achieved remarkable results across many NLP tasks. The present book contains all the articles accepted and published in the Special Issue “Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications” of the MDPI journal Mathematics. A large range of research topics of NLP have been touched upon within this Special Issue, showcasing the diversity and dynamics of this perpetually evolving field, which is providing one of the most important technologies in use today. This Special Issue has provided a platform for researchers to present their novel work in the domain of NLP and its applications, with a focus on applications of machine learning and deep learning in this field. We hope that this will help to foster future research in NLP and all its related 

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
natural language processing; word embeddings; batching; word2vec; curriculum learning; text classification; phrase similarity; part-of-speech tagging; definition extraction; deep learning; ensemble; mathematical domain; Natural Language Processing; educational technology; neural networks; CSCL conversations; string kernels; Mann–Kendall test; Sen’s slope; auto-ARIMA method; paper metadata; research trend; machine reading comprehension; Natural Language Processing; multi-task learning; Self Training; pre-trained model; cross-lingual transfer learning; task-oriented dialogue systems; Arabic language; mixed-language pre-training; multilingual transformer model; mT5; natural language processing; sentiment analysis; aspect detection; temporality; rating; feature evaluation; contradiction intensity; document embeddings; authorship attribution; language modelling; parallel architectures; stylometry; language processing pipelines; sarcasm detection; intermediate-task transfer learning; emotion-enriched sarcasm detection; BERT; transfer learning; multi-task learning; RoBERTa; LSTM; Hate Speech detection; NLP; classification; clustering; text pre-processing; machine learning; National Health Service (NHS); chimp optimization algorithm; course recommendation; E-learning; long short-term memory; random multimodal deep learning; sentiment classification; translationese identification; dialectal varieties; machine translation; feature analysis; neural machine translation; POS tags; MSD tags; inflected language; data sparsity; corpora size; information systems; information retrieval; system effectiveness; search engine; IR system analysis; data analytics; query processing chain; n/a; n/a