Sangam: A Confluence of Knowledge Streams

Current Approaches and Applications in Natural Language Processing

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dc.contributor Montejo-Ráez, Arturo
dc.contributor Jiménez-Zafra, Salud María
dc.date 2022-09-16T13:45:36Z
dc.date 2022-09-16T13:45:36Z
dc.date 2022
dc.date.accessioned 2023-02-18T19:29:11Z
dc.date.available 2023-02-18T19:29:11Z
dc.identifier ONIX_20220916_9783036544397_8
dc.identifier https://directory.doabooks.org/handle/20.500.12854/92022
dc.identifier https://mdpi.com/books/pdfview/book/5892
dc.identifier https://mdpi.com/books/pdfview/book/5892
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/249614
dc.description 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.
dc.format application/octet-stream
dc.language eng
dc.publisher MDPI Books
dc.rights open access
dc.subject natural language processing
dc.subject distributional semantics
dc.subject machine learning
dc.subject language model
dc.subject word embeddings
dc.subject machine translation
dc.subject sentiment analysis
dc.subject quality estimation
dc.subject neural machine translation
dc.subject pretrained language model
dc.subject multilingual pre-trained language model
dc.subject WMT
dc.subject neural networks
dc.subject recurrent neural networks
dc.subject named entity recognition
dc.subject multi-modal dataset
dc.subject Wikimedia Commons
dc.subject multi-modal language model
dc.subject concreteness
dc.subject curriculum learning
dc.subject electronic health records
dc.subject clinical text
dc.subject relationship extraction
dc.subject text classification
dc.subject linguistic corpus
dc.subject deception
dc.subject linguistic cues
dc.subject statistical analysis
dc.subject discriminant function analysis
dc.subject fake news detection
dc.subject stance detection
dc.subject social media
dc.subject abstractive summarization
dc.subject monolingual models
dc.subject multilingual models
dc.subject transformer models
dc.subject transfer learning
dc.subject discourse analysis
dc.subject problem–solution pattern
dc.subject automatic classification
dc.subject machine learning classifiers
dc.subject deep neural networks
dc.subject question answering
dc.subject machine reading comprehension
dc.subject query expansion
dc.subject information retrieval
dc.subject multinomial naive bayes
dc.subject relevance feedback
dc.subject cause-effect relation
dc.subject transitive closure
dc.subject word co-occurrence
dc.subject automatic hate speech detection
dc.subject multisource feature extraction
dc.subject Latin American Spanish language models
dc.subject fine-grained named entity recognition
dc.subject k-stacked feature fusion
dc.subject dual-stacked output
dc.subject unbalanced data problem
dc.subject document representation
dc.subject semantic analysis
dc.subject conceptual modeling
dc.subject universal representation
dc.subject trend analysis
dc.subject topic modeling
dc.subject Bert
dc.subject geospatial data technology and application
dc.subject attention model
dc.subject dual multi-head attention
dc.subject inter-information relationship
dc.subject question difficult estimation
dc.subject named-entity recognition
dc.subject BERT model
dc.subject conditional random field
dc.subject pre-trained model
dc.subject fine-tuning
dc.subject feature fusion
dc.subject attention mechanism
dc.subject task-oriented dialogue systems
dc.subject Arabic
dc.subject multi-lingual transformer model
dc.subject mT5
dc.subject language marker
dc.subject mental disorder
dc.subject deep learning
dc.subject LIWC
dc.subject spaCy
dc.subject RobBERT
dc.subject fastText
dc.subject LIME
dc.subject conversational AI
dc.subject intent detection
dc.subject slot filling
dc.subject retrieval-based question answering
dc.subject query generation
dc.subject entity linking
dc.subject knowledge graph
dc.subject entity embedding
dc.subject global model
dc.subject DISC model
dc.subject personality recognition
dc.subject predictive model
dc.subject text analysis
dc.subject data privacy
dc.subject federated learning
dc.subject transformer
dc.subject n/a
dc.subject bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
dc.subject bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
dc.title Current Approaches and Applications in Natural Language Processing
dc.resourceType book
dc.alternateIdentifier 9783036544397
dc.alternateIdentifier 9783036544403
dc.alternateIdentifier 10.3390/books978-3-0365-4440-3
dc.licenseCondition Attribution 4.0 International
dc.identifierdoi 10.3390/books978-3-0365-4440-3
dc.relationisPublishedBy 430a2802-f250-43a2-9a76-b343750a1dfa
dc.relationisbn 9783036544397
dc.relationisbn 9783036544403
dc.pages 476
dc.placepublication Basel


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