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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications

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Current approaches to Natural Language Processing (NLP) have shown impressive improvements in many important tasks: machine translation, language modeling, text generation, sentiment/emotion analysis, natural language understanding, and question answering, among others. The advent of new methods and techniques, such as graph-based approaches, reinforcement learning, or deep learning, have boosted many NLP tasks to a human-level performance (and even beyond). This has attracted the interest of many companies, so new products and solutions can benefit from advances in this relevant area within the artificial intelligence domain.This Special Issue reprint, focusing on emerging techniques and trendy applications of NLP methods, reports on some of these achievements, establishing a useful reference for industry and researchers on cutting-edge human language technologies.

Technology: general issues --- History of engineering & technology --- natural language processing --- distributional semantics --- machine learning --- language model --- word embeddings --- machine translation --- sentiment analysis --- quality estimation --- neural machine translation --- pretrained language model --- multilingual pre-trained language model --- WMT --- neural networks --- recurrent neural networks --- named entity recognition --- multi-modal dataset --- Wikimedia Commons --- multi-modal language model --- concreteness --- curriculum learning --- electronic health records --- clinical text --- relationship extraction --- text classification --- linguistic corpus --- deception --- linguistic cues --- statistical analysis --- discriminant function analysis --- fake news detection --- stance detection --- social media --- abstractive summarization --- monolingual models --- multilingual models --- transformer models --- transfer learning --- discourse analysis --- problem–solution pattern --- automatic classification --- machine learning classifiers --- deep neural networks --- question answering --- machine reading comprehension --- query expansion --- information retrieval --- multinomial naive bayes --- relevance feedback --- cause-effect relation --- transitive closure --- word co-occurrence --- automatic hate speech detection --- multisource feature extraction --- Latin American Spanish language models --- fine-grained named entity recognition --- k-stacked feature fusion --- dual-stacked output --- unbalanced data problem --- document representation --- semantic analysis --- conceptual modeling --- universal representation --- trend analysis --- topic modeling --- Bert --- geospatial data technology and application --- attention model --- dual multi-head attention --- inter-information relationship --- question difficult estimation --- named-entity recognition --- BERT model --- conditional random field --- pre-trained model --- fine-tuning --- feature fusion --- attention mechanism --- task-oriented dialogue systems --- Arabic --- multi-lingual transformer model --- mT5 --- language marker --- mental disorder --- deep learning --- LIWC --- spaCy --- RobBERT --- fastText --- LIME --- conversational AI --- intent detection --- slot filling --- retrieval-based question answering --- query generation --- entity linking --- knowledge graph --- entity embedding --- global model --- DISC model --- personality recognition --- predictive model --- text analysis --- data privacy --- federated learning --- transformer --- n/a --- problem-solution pattern