Current Trends in Natural Language Processing (NLP) and Human Language Technology (HLT)

doi:10.3390/books978-3-7258-0086-5

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Current Trends in Natural Language Processing (NLP) and Human Language Technology (HLT)

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March 2024

262 pages

ISBN978-3-7258-0085-8 (Hardback)
ISBN978-3-7258-0086-5 (PDF)

https://doi.org/10.3390/books978-3-7258-0086-5

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This book is a reprint of the Special Issue Current Trends in Natural Language Processing (NLP) and Human Language Technology (HLT) that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Public Health & Healthcare

Summary

Natural language processing (NLP) is a crucial technology in use today, particularly due to the vast and increasing amount of online text that requires comprehension to fully realize its value. Human language technology (HLT) poses a significant challenge for computing, with it requiring advanced NLP and the availability of big data to create large-scale systems and applications. Researching software and systems that bridge the linguistic gap between people and machines requires knowledge of natural language processing (NLP) and computational linguistics (CL), including their application-oriented aspects. This Reprint contains all accepted articles published in the Special Issue “Current Trends in Natural Language Processing (NLP) and Human Language Technology (HLT)”. The aim of this Special Issue was to focus on technologies for processing human language in the form of text, using natural language processing (NLP) tools and techniques to address the two main challenges: natural language understanding and natural language generation. This Special Issue presents innovative research in the domain of NLP and HLT. It is our hope that the research results will contribute to fostering future research in NLP and inspiring future studies in related fields.

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Keywords

paraphrase identification; transformers; cross-encoders; bi-encoders; intent classification; chatbot; few-shot learning; data augmentation; online clustering; data projection; text simplification; deep learning; reinforcement learning; readability level; data augmentation; stancerecognition; multilingual models; online debates; public consultations; natural language processing; transformers; generative pre-trained transformer; GPT; ChatGPT; self-supervised learning; deep learning; natural language processing; NLP; neural machine translation; statistical machine translation; sentence embedding; similarity; classification; hybrid machine translation; multiword expression identification; multilingual; lateral inhibition; domain adaptation; PARSEME corpus; relation classification; graph neural network; cybersecurity; natural language processing; natural language processing; task-oriented dialogue system; PEFT; fine-tuning; training efficiency; triple extraction; entity recognition; relation extraction; joint extraction; sentiment analysis; aspect-based sentiment analysis; difficulty; sentiment polarity; text representation; language modeling; language models; composite structures; machine learning; Serbian language; text classification; hate speech detection; zero-shot learning; few-shot learning; fine-tuning; large language models; natural language processing