Deep Learning and Linguistic Representation
portes grátis
Deep Learning and Linguistic Representation
Lappin, Shalom
Taylor & Francis Ltd
04/2021
168
Mole
Inglês
9780367648749
15 a 20 dias
240
Descrição não disponível.
Chapter 1 Introduction: Deep Learning in Natural Language Processing
1.1 OUTLINE OF THE BOOK
1.2 FROM ENGINEERING TO COGNITIVE SCIENCE
1.3 ELEMENTS OF DEEP LEARNING
1.4 TYPES OF DEEP NEURAL NETWORKS
1.5 AN EXAMPLE APPLICATION
1.6 SUMMARY AND CONCLUSIONS
Chapter 2 Learning Syntactic Structure with Deep Neural Networks
2.1 SUBJECT-VERB AGREEMENT
2.2 ARCHITECTURE AND EXPERIMENTS
2.3 HIERARCHICAL STRUCTURE
2.4 TREE DNNS
2.5 SUMMARY AND CONCLUSIONS
Chapter 3 Machine Learning and The Sentence Acceptability Task
3.1 GRADIENCE IN SENTENCE ACCEPTABILITY
3.2 PREDICTING ACCEPTABILITY WITH MACHINE LEARNING MODELS
3.3 ADDING TAGS AND TREES
3.4 SUMMARY AND CONCLUSIONS
Chapter 4 Predicting Human Acceptability Judgments in Context
4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT
4.2 TWO SETS OF EXPERIMENTS
4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE
4.4 PREDICTING ACCEPTABILITY WITH DIFFERENT DNN MODELS
4.5 SUMMARY AND CONCLUSIONS
Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge
5.1 HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS?
5.2 MACHINE LEARNING MODELS VS FORMAL GRAMMAR
5.3 EXPLAINING LANGUAGE ACQUISITION
5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 1
5.5 SUMMARY AND CONCLUSIONS
Chapter 6 Conclusions and Future Work
6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE
6.2 DOMAIN SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION
6.3 DIRECTIONS FOR FUTURE WORK
REFERENCES
Author Index
Subject Index
1.1 OUTLINE OF THE BOOK
1.2 FROM ENGINEERING TO COGNITIVE SCIENCE
1.3 ELEMENTS OF DEEP LEARNING
1.4 TYPES OF DEEP NEURAL NETWORKS
1.5 AN EXAMPLE APPLICATION
1.6 SUMMARY AND CONCLUSIONS
Chapter 2 Learning Syntactic Structure with Deep Neural Networks
2.1 SUBJECT-VERB AGREEMENT
2.2 ARCHITECTURE AND EXPERIMENTS
2.3 HIERARCHICAL STRUCTURE
2.4 TREE DNNS
2.5 SUMMARY AND CONCLUSIONS
Chapter 3 Machine Learning and The Sentence Acceptability Task
3.1 GRADIENCE IN SENTENCE ACCEPTABILITY
3.2 PREDICTING ACCEPTABILITY WITH MACHINE LEARNING MODELS
3.3 ADDING TAGS AND TREES
3.4 SUMMARY AND CONCLUSIONS
Chapter 4 Predicting Human Acceptability Judgments in Context
4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT
4.2 TWO SETS OF EXPERIMENTS
4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE
4.4 PREDICTING ACCEPTABILITY WITH DIFFERENT DNN MODELS
4.5 SUMMARY AND CONCLUSIONS
Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge
5.1 HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS?
5.2 MACHINE LEARNING MODELS VS FORMAL GRAMMAR
5.3 EXPLAINING LANGUAGE ACQUISITION
5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 1
5.5 SUMMARY AND CONCLUSIONS
Chapter 6 Conclusions and Future Work
6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE
6.2 DOMAIN SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION
6.3 DIRECTIONS FOR FUTURE WORK
REFERENCES
Author Index
Subject Index
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
DNN Model;Linguistic representation;POS Tag;Human learning;HMM.;Deep learning methods;Average Spearman Correlation;Artificial intelligence;Test Set;Natural language processing;Context Vector;Annotated Training Data;DNN Learning;Integrated Data Structure;Dl Method;Random Context;Max Pooling Layer;Syntactic Tags;Scoring Accuracy Rates;Tensor Operations;Subject Verb Agreement;Non-parametric Wilcoxon Signed Rank Test;Preceding Target Words;Machine Translation;Acceptability Judgements;Pearson Coefficient Correlation;Unsupervised Machine Learning;Hierarchical Syntactic Structure;Training Set Increases;Document Context
Chapter 1 Introduction: Deep Learning in Natural Language Processing
1.1 OUTLINE OF THE BOOK
1.2 FROM ENGINEERING TO COGNITIVE SCIENCE
1.3 ELEMENTS OF DEEP LEARNING
1.4 TYPES OF DEEP NEURAL NETWORKS
1.5 AN EXAMPLE APPLICATION
1.6 SUMMARY AND CONCLUSIONS
Chapter 2 Learning Syntactic Structure with Deep Neural Networks
2.1 SUBJECT-VERB AGREEMENT
2.2 ARCHITECTURE AND EXPERIMENTS
2.3 HIERARCHICAL STRUCTURE
2.4 TREE DNNS
2.5 SUMMARY AND CONCLUSIONS
Chapter 3 Machine Learning and The Sentence Acceptability Task
3.1 GRADIENCE IN SENTENCE ACCEPTABILITY
3.2 PREDICTING ACCEPTABILITY WITH MACHINE LEARNING MODELS
3.3 ADDING TAGS AND TREES
3.4 SUMMARY AND CONCLUSIONS
Chapter 4 Predicting Human Acceptability Judgments in Context
4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT
4.2 TWO SETS OF EXPERIMENTS
4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE
4.4 PREDICTING ACCEPTABILITY WITH DIFFERENT DNN MODELS
4.5 SUMMARY AND CONCLUSIONS
Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge
5.1 HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS?
5.2 MACHINE LEARNING MODELS VS FORMAL GRAMMAR
5.3 EXPLAINING LANGUAGE ACQUISITION
5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 1
5.5 SUMMARY AND CONCLUSIONS
Chapter 6 Conclusions and Future Work
6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE
6.2 DOMAIN SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION
6.3 DIRECTIONS FOR FUTURE WORK
REFERENCES
Author Index
Subject Index
1.1 OUTLINE OF THE BOOK
1.2 FROM ENGINEERING TO COGNITIVE SCIENCE
1.3 ELEMENTS OF DEEP LEARNING
1.4 TYPES OF DEEP NEURAL NETWORKS
1.5 AN EXAMPLE APPLICATION
1.6 SUMMARY AND CONCLUSIONS
Chapter 2 Learning Syntactic Structure with Deep Neural Networks
2.1 SUBJECT-VERB AGREEMENT
2.2 ARCHITECTURE AND EXPERIMENTS
2.3 HIERARCHICAL STRUCTURE
2.4 TREE DNNS
2.5 SUMMARY AND CONCLUSIONS
Chapter 3 Machine Learning and The Sentence Acceptability Task
3.1 GRADIENCE IN SENTENCE ACCEPTABILITY
3.2 PREDICTING ACCEPTABILITY WITH MACHINE LEARNING MODELS
3.3 ADDING TAGS AND TREES
3.4 SUMMARY AND CONCLUSIONS
Chapter 4 Predicting Human Acceptability Judgments in Context
4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT
4.2 TWO SETS OF EXPERIMENTS
4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE
4.4 PREDICTING ACCEPTABILITY WITH DIFFERENT DNN MODELS
4.5 SUMMARY AND CONCLUSIONS
Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge
5.1 HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS?
5.2 MACHINE LEARNING MODELS VS FORMAL GRAMMAR
5.3 EXPLAINING LANGUAGE ACQUISITION
5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 1
5.5 SUMMARY AND CONCLUSIONS
Chapter 6 Conclusions and Future Work
6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE
6.2 DOMAIN SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION
6.3 DIRECTIONS FOR FUTURE WORK
REFERENCES
Author Index
Subject Index
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
DNN Model;Linguistic representation;POS Tag;Human learning;HMM.;Deep learning methods;Average Spearman Correlation;Artificial intelligence;Test Set;Natural language processing;Context Vector;Annotated Training Data;DNN Learning;Integrated Data Structure;Dl Method;Random Context;Max Pooling Layer;Syntactic Tags;Scoring Accuracy Rates;Tensor Operations;Subject Verb Agreement;Non-parametric Wilcoxon Signed Rank Test;Preceding Target Words;Machine Translation;Acceptability Judgements;Pearson Coefficient Correlation;Unsupervised Machine Learning;Hierarchical Syntactic Structure;Training Set Increases;Document Context
