Formal Analysis for Natural Language Processing: A Handbook

ISBN-13: 9789811651717 / Angielski / Twarda / 2023

Zhiwei Feng

Spis treści

Part One  History Review

 

Chapter One

Past and Present of Natural Language Processing                      

1.1 What is natural language processing        

1.2 History review of natural language processing                      

1.3 Characteristics of current trends in natural language processing                        

References                           

 

Chapter Two

Pioneers in the Study of Language Computing            

2.1 Markov chains                             

2.2 Zipf’s law                     

2.3 Shannon's work on entropy                       

2.4 Bar-Hillel’s category grammar                                  

2.5 Harris’s approach of linguistic string analysis                        

2.6 О.С.Кулагина’s linguistic set theory model          

References

 

Part Two Formal Models

               

Chapter Three

Formal Models Based on Phrase Structure Grammar

3.1 Chomsky’s hierarchy of grammar                           

3.2 Finite-state grammar and its limitations                  

3.3 Phrase structure grammar          

3.4 Recursive transition networks and augmented transition networks  

3.5 Bottom-up and top-down analysis          

3.6 General syntactic processor and chart parsing       

3.7 Earley algorithm                          

3.8 Left corner analysis                     

3.9 Cocke-Younger-Kasami algorithm          

3.10 Tomita algorithm                      

3.11 Government and binding theory and minimalist program                

3.12 Joshi’s tree-adjoining grammar                              

3.13 Formal description of the structure of Chinese characters                

3.14 Hausser’s left-associative grammar      

References           

 

Chapter Four

Formal Models Based on Unification            

4.1 Multiple branched and multiple labeled tree analysis (MMT)            

4.2 Kaplan’s lexical functional grammar      

4.3 Martin Kay’s functional unification grammar      

4.4 Gazdar’s generalized phrase structure grammar   

4.5 Shieber’s PATR                            

4.6 Pollard’s head-driven phrase structure grammar   

4.7 Pereira &Warren’s definite clause grammar           

References                           

 

Chapter Five

Formal Models Based on Dependency and Valence                  

5.1 Origin of valence          

5.2 Tesnière’s dependency grammar              

5.3 Application of dependency grammar in natural language processing              

5.4 Valence grammar        

5.5 Application of valence grammar in natural language processing      

References                           

               

Chapter Six

Formal Models Based on Lexicalism             

6.1 Gross’ lexicon-grammar             

6.2 Chain grammar            

6.3 Lexical semantics        

6.4 Ontology        

6.5 WordNet        

6.6 HowNet         

6.7 Pustejovesky’s generative lexicon theory               

References          

 

Chapter Seven

Formal Models of Automatic Semantic Processing     

7.1 Sememe analysis         

7.2 Semantic field              

7.3 Semantic network        

7.4 Montague’s semantics                

7.5 Wilks’ preference semantics      

7.6 Schank’s conceptual dependency theory               

7.7 Mel'chuk’s meaning-text theory

7.8 Fillmore’s deep case and frame semantics             

7.9 Word sense disambiguation methods      

References                           

 

Chapter Eight

 Formal Models of Automatic Situation and Pragmatic Processing

8.1 Basic concepts of systemic functional grammar                  

8.2 Application of systemic functional grammar in natural language processing

8.3 Speech act theory and conversation intelligent agent 

References

 

Chapter Nine       

Formal models of Discourse Analysis

9.1 Reference resolution

9.2 Reasoning techniques in text coherence

9.3 Mann & Thompson’s rhetorical structure theory                  

References

                               

               

Chapter Ten

 Formal Models of Probabilistic Grammar    

10.1 Probabilistic context-free grammar and sentence ambiguity           

10.2 Fundamentals of probabilistic context-free grammar      

10.3 Three assumptions of probabilistic context-free grammar               

10.4 Probabilistic lexicalized context-free grammar   

References           

 

 

Chapter Eleven

 Formal Models of Neural Network and Deep Learning             

11.1 Development of neural network

11.2 Brain neural network and artificial neural network            

11.3 Machine learning and deep learning

11.4 Word vector and word embedding (CBOW, Skip-gram)

11.5 Dense word vector (Word2vec)

11.6 Perceptron   

11.7 Feed-forward Neural Network (FNN)    

11.8 Convolutional Neural Network (CNN)  

11.9 Recurrent Neural Network (RNN)

11.10 Attention mechanism

11.11 External memory

11.12 Pre-training models (Transformer and BERT)

References

 

Chapter Twelve

 Knowledge Graph

12.1 Knowledge graph and deep learning

12.2 Knowledge representation

12.3 Entity recognition

12.4 Entity disambiguation

12.5 Relation abstraction

12.6 Entity abstraction

12.7 Knowledge storage

12.8 Knowledge inference

References

 

Conclusion

O autorze

Feng Zhiwei is a computational linguist and senior research fellow at the Institute of Applied Linguistics, Ministry of Education, China. He has a broad and extensive background in linguistics, mathematics and computer science, and has been engaged in interdisciplinary research in linguistics, mathematics and computer science for more than 50 years. One of the first natural language processing and computational linguistics scholars in China, he has published more than 30 books and more than 400 papers in China and abroad. He is the winner of the NLPCC (Natural Language Processing & Chinese Computing) Distinguished Achievement Award of the CCF (China Computer Federation) in 2018.

Blurb

The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. NLP is now rapidly evolving, as new methods and toolsets converge with an ever-expanding wealth of available data. This state-of-the-art handbook addresses all aspects of formal analysis for natural language processing. Following a review of the field’s history, it systematically introduces readers to the rule-based model, statistical model, neural network model, and pre-training model in natural language processing. 

At a time characterized by the steady and vigorous growth of natural language processing, this handbook provides a highly accessible introduction and much-needed reference guide to both the theory and method of NLP. It can be used for individual study, as the textbook for courses on natural language processing or computational linguistics, or as a supplement to courses on artificial intelligence, and offers a valuable asset for researchers, practitioners, lecturers, graduate and undergraduate students alike.