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Current Approaches and Applications in Natural Language Processing
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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.
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Keywords
- abstractive summarization
- Arabic
- attention mechanism
- attention model
- automatic classification
- automatic hate speech detection
- BERT
- BERT Model
- cause-effect relation
- clinical text
- conceptual modeling
- concreteness
- conditional random field
- conversational AI
- curriculum learning
- data privacy
- Deception
- deep learning
- deep neural networks
- DISC model
- Discourse analysis
- discriminant function analysis
- Distributional semantics
- Document Representation
- dual multi-head attention
- dual-stacked output
- Electronic Health Records
- entity embedding
- entity linking
- fake news detection
- fastText
- feature fusion
- federated learning
- fine-grained named entity recognition
- fine-tuning
- geospatial data technology and application
- global model
- History of engineering & technology
- Information retrieval
- intent detection
- inter-information relationship
- k-stacked feature fusion
- knowledge graph
- language marker
- language model
- Latin American Spanish language models
- Lime
- linguistic corpus
- linguistic cues
- LIWC
- Machine learning
- machine learning classifiers
- machine reading comprehension
- machine translation
- mental disorder
- monolingual models
- mT5
- multi-lingual transformer model
- multi-modal dataset
- multi-modal language model
- multilingual models
- multilingual pre-trained language model
- multinomial naive bayes
- multisource feature extraction
- n/a
- named entity recognition
- named-entity recognition
- Natural Language Processing
- neural machine translation
- neural networks
- personality recognition
- pre-trained model
- predictive model
- pretrained language model
- problem–solution pattern
- quality estimation
- query expansion
- query generation
- question answering
- question difficult estimation
- recurrent neural networks
- relationship extraction
- relevance feedback
- retrieval-based question answering
- RobBERT
- semantic analysis
- sentiment analysis
- slot filling
- Social media
- spaCy
- stance detection
- statistical analysis
- task-oriented dialogue systems
- Technology, engineering, agriculture
- Technology: general issues
- Text Analysis
- text classification
- topic modeling
- transfer learning
- transformer
- transformer models
- transitive closure
- Trend Analysis
- unbalanced data problem
- universal representation
- Wikimedia Commons
- WMT
- word co-occurrence
- word embeddings
Links
DOI: 10.3390/books978-3-0365-4440-3Editions