Current Approaches and Applications in Natural Language Processing

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...

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Bibliographic Details
Format:	Online
Language:	English
Published:	2022
Subjects:	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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online Access:	ONIX_20220916_9783036544397_8
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Summary:	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.

Current Approaches and Applications in Natural Language Processing

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