Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications
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portes grátis
Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications
Gudivada, Venkat N.; Rao, C.R.
Elsevier Science & Technology
08/2018
537
Dura
Inglês
9780444640420
15 a 20 dias
980
Descrição não disponível.
1. Linguistics: Core Concepts and Principles 2. Grammars 3. Open-Source Libraries, Application Frameworks, Workflow Systems, and Other Resources 4. Mathematical Essentials 5. Probability 6. Inference and Prediction Methods 7. Random Processes 8. Bayesian Methods 9. Machine Learning 10. Artificial Neural Networks for Natural Language Processing 11. Information Retrieval 12. Language Core Tasks 1 13. Language Core Tasks 2 14. Language Understanding Applications 1 15. Language Understanding Applications 2 16. Deep Learning for Natural Language Processing 17. Text Mining for Modeling Cyberattacks 18. World Languages and Crosslinguistics 19. Linguistic Elegance of the Languages of South India 20. Current Trends and Open Problems
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
AI techniques; Annotated corpora; Attack graphs; Average mutual information; Average self-information; Bayesian methods; Bayesian networks; BM25; Boolean information retrieval; Chomsky hierarchy; Classical Languages; Classification of functions; CNN; Conditional self-information; Context-free languages; Context-sensitive languages; Convolutional neural networks; Cybersecurity; DAG graphs; Deep learning; Dimension of a vector space; Divergence from randomness model; Document embedding; Document visualization; Dravidian Languages; Eigenvalues; Eigen-vectors; English language grammar; Entropy; Functions; Global optima; Gram-Schmidt orthogonalization; Hierarchical clustering; Hypothesis testing; Information extraction; Information retrieval; Inner product; IR models; Joint entropy; Language identification; Language model; Language modeling; Linear transformation; Linearly independent; Linguistics; Local optima; Machine translation; Markov chain Monte Carlo methods; Markov networks; Matrix representation of a linear transformation; Morphology; Mutual information; Named entity recognition; Natural language processing; Natural language understanding; Natural language user interfaces; Natural languages; Neural network; NLP; Open-source libraries; Operations on functions; Ordered basis; Orthogonal projection; Orthogonality; Parsing; Part of Speech tagging; Phonetics; Phonology; Prediction; Probability distributions; Probability of relevance framework; Probability theory; Programming language grammar; Projections; Question-answering systems; Random variables; Regular languages; Regularization; Relevance feedback; Self-information; Semantic structures; Semantics; Sentence embedding; Sentence parsing; Sequence modeling; Spoken languages; Statistical inference; Syntactic structures; Syntax; Text mining; Text segmentation; TF-IDF; Vector space; Vector space model; Word segmentation; Word vectors; Word-sense disambiguation; Workflow systems; Written languages
1. Linguistics: Core Concepts and Principles 2. Grammars 3. Open-Source Libraries, Application Frameworks, Workflow Systems, and Other Resources 4. Mathematical Essentials 5. Probability 6. Inference and Prediction Methods 7. Random Processes 8. Bayesian Methods 9. Machine Learning 10. Artificial Neural Networks for Natural Language Processing 11. Information Retrieval 12. Language Core Tasks 1 13. Language Core Tasks 2 14. Language Understanding Applications 1 15. Language Understanding Applications 2 16. Deep Learning for Natural Language Processing 17. Text Mining for Modeling Cyberattacks 18. World Languages and Crosslinguistics 19. Linguistic Elegance of the Languages of South India 20. Current Trends and Open Problems
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
AI techniques; Annotated corpora; Attack graphs; Average mutual information; Average self-information; Bayesian methods; Bayesian networks; BM25; Boolean information retrieval; Chomsky hierarchy; Classical Languages; Classification of functions; CNN; Conditional self-information; Context-free languages; Context-sensitive languages; Convolutional neural networks; Cybersecurity; DAG graphs; Deep learning; Dimension of a vector space; Divergence from randomness model; Document embedding; Document visualization; Dravidian Languages; Eigenvalues; Eigen-vectors; English language grammar; Entropy; Functions; Global optima; Gram-Schmidt orthogonalization; Hierarchical clustering; Hypothesis testing; Information extraction; Information retrieval; Inner product; IR models; Joint entropy; Language identification; Language model; Language modeling; Linear transformation; Linearly independent; Linguistics; Local optima; Machine translation; Markov chain Monte Carlo methods; Markov networks; Matrix representation of a linear transformation; Morphology; Mutual information; Named entity recognition; Natural language processing; Natural language understanding; Natural language user interfaces; Natural languages; Neural network; NLP; Open-source libraries; Operations on functions; Ordered basis; Orthogonal projection; Orthogonality; Parsing; Part of Speech tagging; Phonetics; Phonology; Prediction; Probability distributions; Probability of relevance framework; Probability theory; Programming language grammar; Projections; Question-answering systems; Random variables; Regular languages; Regularization; Relevance feedback; Self-information; Semantic structures; Semantics; Sentence embedding; Sentence parsing; Sequence modeling; Spoken languages; Statistical inference; Syntactic structures; Syntax; Text mining; Text segmentation; TF-IDF; Vector space; Vector space model; Word segmentation; Word vectors; Word-sense disambiguation; Workflow systems; Written languages
