The handbook of computational linguistics and natural language processing /

Subjects: An electronic book accessible through the World Wide Web; click to view

The handbook of computational linguistics and natural language processing /

Subjects: An electronic book accessible through the World Wide Web; click to view

Deep learning and linguistic representation / by Lappin, Shalom

Table of Contents: “…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 Context4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT 4.2 TWO SETS OF EXPERIMENTS 4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE4.4 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? …”
Taylor & Francis
OCLC metadata license agreement

Deep learning and linguistic representation / by Lappin, Shalom

Table of Contents: “…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 Context4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT 4.2 TWO SETS OF EXPERIMENTS 4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE4.4 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? …”
Taylor & Francis
OCLC metadata license agreement