Python natural language processing :: explore NLP with machine learning and deep learning techniques /
Chapter 6: Advanced Feature Engineering and NLP Algorithms -- Recall word embedding -- Understanding the basics of word2vec -- Distributional semantics -- Defining word2vec -- Necessity of unsupervised distribution semantic model - word2vec -- Challenges -- Converting the word2vec model from black b...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Chapter 6: Advanced Feature Engineering and NLP Algorithms -- Recall word embedding -- Understanding the basics of word2vec -- Distributional semantics -- Defining word2vec -- Necessity of unsupervised distribution semantic model - word2vec -- Challenges -- Converting the word2vec model from black box to white box -- Distributional similarity based representation -- Understanding the components of the word2vec model -- Input of the word2vec -- Output of word2vec -- Construction components of the word2vec model -- Architectural component -- Understanding the logic of the word2vec model -- Vocabulary builder -- Context builder -- Neural network with two layers -- Structural details of a word2vec neural network -- Word2vec neural network layer's details -- Softmax function -- Main processing algorithms -- Continuous bag of words -- Skip-gram -- Understanding algorithmic techniques and the mathematics behind the word2vec model -- Understanding the basic mathematics for the word2vec algorithm -- Techniques used at the vocabulary building stage -- Lossy counting -- Using it at the stage of vocabulary building -- Applications -- Techniques used at the context building stage -- Dynamic window scaling -- Understanding dynamic context window techniques -- Subsampling -- Pruning -- Algorithms used by neural networks -- Structure of the neurons -- Basic neuron structure -- Training a simple neuron -- Define error function -- Understanding gradient descent in word2vec -- Single neuron application -- Multi-layer neural networks -- Backpropagation -- Mathematics behind the word2vec model -- Techniques used to generate final vectors and probability prediction stage -- Hierarchical softmax -- Negative sampling -- Some of the facts related to word2vec -- Applications of word2vec -- Implementation of simple examples -- Famous example (king - man + woman). |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781787285521 1787285529 9781523112173 1523112174 |
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| 505 | 0 | |a Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction -- Understanding natural language processing -- Understanding basic applications -- Understanding advanced applications -- Advantages of togetherness -- NLP and Python -- Environment setup for NLTK -- Tips for readers -- Summary -- Chapter 2: Practical Understanding of a Corpus and Dataset -- What is a corpus? -- Why do we need a corpus? -- Understanding corpus analysis -- Exercise -- Understanding types of data attributes -- Categorical or qualitative data attributes -- Numeric or quantitative data attributes -- Exploring different file formats for corpora -- Resources for accessing free corpora -- Preparing a dataset for NLP applications -- Selecting data -- Preprocessing the dataset -- Formatting -- Cleaning -- Sampling -- Transforming data -- Web scraping -- Summary -- Chapter 3: Understanding the Structure of a Sentences -- Understanding components of NLP -- Natural language understanding -- Natural language generation -- Differences between NLU and NLG -- Branches of NLP -- Defining context-free grammar -- Exercise -- Morphological analysis -- What is morphology? -- What are morphemes? -- What is a stem? -- What is morphological analysis? -- What is a word? -- Classification of morphemes -- Free morphemes -- Bound morphemes -- Derivational morphemes -- Inflectional morphemes -- What is the difference between a stem and a root? -- Exercise -- Lexical analysis -- What is a token? -- What are part of speech tags? -- Process of deriving tokens -- Difference between stemming and lemmatization -- Applications -- Syntactic analysis -- What is syntactic analysis? -- Semantic analysis -- What is semantic analysis? -- Lexical semantics. | |
| 505 | 8 | |a Hyponymy and hyponyms -- Homonymy -- Polysemy -- What is the difference between polysemy and homonymy? -- Application of semantic analysis -- Handling ambiguity -- Lexical ambiguity -- Syntactic ambiguity -- Approach to handle syntactic ambiguity -- Semantic ambiguity -- Pragmatic ambiguity -- Discourse integration -- Applications -- Pragmatic analysis -- Summary -- Chapter 4: Preprocessing -- Handling corpus-raw text -- Getting raw text -- Lowercase conversion -- Sentence tokenization -- Challenges of sentence tokenization -- Stemming for raw text -- Challenges of stemming for raw text -- Lemmatization of raw text -- Challenges of lemmatization of raw text -- Stop word removal -- Exercise -- Handling corpus-raw sentences -- Word tokenization -- Challenges for word tokenization -- Word lemmatization -- Challenges for word lemmatization -- Basic preprocessing -- Regular expressions -- Basic level regular expression -- Basic flags -- Advanced level regular expression -- Positive lookahead -- Positive lookbehind -- Negative lookahead -- Negative lookbehind -- Practical and customized preprocessing -- Decide by yourself -- Is preprocessing required? -- What kind of preprocessing is required? -- Understanding case studies of preprocessing -- Grammar correction system -- Sentiment analysis -- Machine translation -- Spelling correction -- Approach -- Summary -- Chapter 5: Feature Engineering and NLP Algorithms -- Understanding feature engineering -- What is feature engineering? -- What is the purpose of feature engineering? -- Challenges -- Basic feature of NLP -- Parsers and parsing -- Understanding the basics of parsers -- Understanding the concept of parsing -- Developing a parser from scratch -- Types of grammar -- Context-free grammar -- Probabilistic context-free grammar -- Calculating the probability of a tree. | |
| 505 | 8 | |a Calculating the probability of a string -- Grammar transformation -- Developing a parser with the Cocke-Kasami-Younger Algorithm -- Developing parsers step-by-step -- Existing parser tools -- The Stanford parser -- The spaCy parser -- Extracting and understanding the features -- Customizing parser tools -- Challenges -- POS tagging and POS taggers -- Understanding the concept of POS tagging and POS taggers -- Developing POS taggers step-by-step -- Plug and play with existing POS taggers -- A Stanford POS tagger example -- Using polyglot to generate POS tagging -- Exercise -- Using POS tags as features -- Challenges -- Name entity recognition -- Classes of NER -- Plug and play with existing NER tools -- A Stanford NER example -- A Spacy NER example -- Extracting and understanding the features -- Challenges -- n-grams -- Understanding n-gram using a practice example -- Application -- Bag of words -- Understanding BOW -- Understanding BOW using a practical example -- Comparing n-grams and BOW -- Applications -- Semantic tools and resources -- Basic statistical features for NLP -- Basic mathematics -- Basic concepts of linear algebra for NLP -- Basic concepts of the probabilistic theory for NLP -- Probability -- Independent event and dependent event -- Conditional probability -- TF-IDF -- Understanding TF-IDF -- Understanding TF-IDF with a practical example -- Using textblob -- Using scikit-learn -- Application -- Vectorization -- Encoders and decoders -- One-hot encoding -- Understanding a practical example for one-hot encoding -- Application -- Normalization -- The linguistics aspect of normalization -- The statistical aspect of normalization -- Probabilistic models -- Understanding probabilistic language modeling -- Application of LM -- Indexing -- Application -- Ranking -- Advantages of features engineering -- Challenges of features engineering. | |
| 505 | 8 | |a Advantages of word2vec -- Challenges of word2vec -- How is word2vec used in real-life applications? -- When should you use word2vec? -- Developing something interesting -- Exercise -- Extension of the word2vec concept -- Para2Vec -- Doc2Vec -- Applications of Doc2vec -- GloVe -- Exercise -- Importance of vectorization in deep learning -- Summary -- Chapter 7: Rule-Based System for NLP -- Understanding of the rule-based system -- What does the RB system mean? -- Purpose of having the rule-based system -- Why do we need the rule-based system? -- Which kind of applications can use the RB approach over the other approaches? -- Exercise -- What kind of resources do you need if you want to develop a rule-based system? -- Architecture of the RB system -- General architecture of the rule-based system as an expert system -- Practical architecture of the rule-based system for NLP applications -- Custom architecture -- the RB system for NLP applications -- Exercise -- Apache UIMA -- the RB system for NLP applications -- Understanding the RB system development life cycle -- Applications -- NLP applications using the rule-based system -- Generalized AI applications using the rule-based system -- Developing NLP applications using the RB system -- Thinking process for making rules -- Start with simple rules -- Scraping the text data -- Defining the rule for our goal -- Coding our rule and generating a prototype and result -- Exercise -- Python for pattern-matching rules for a proofreading application -- Exercise -- Grammar correction -- Template-based chatbot application -- Flow of code -- Advantages of template-based chatbot -- Disadvantages of template-based chatbot -- Exercise -- Comparing the rule-based approach with other approaches -- Advantages of the rule-based system -- Disadvantages of the rule-based system -- Challenges for the rule-based system. | |
| 520 | |a Chapter 6: Advanced Feature Engineering and NLP Algorithms -- Recall word embedding -- Understanding the basics of word2vec -- Distributional semantics -- Defining word2vec -- Necessity of unsupervised distribution semantic model - word2vec -- Challenges -- Converting the word2vec model from black box to white box -- Distributional similarity based representation -- Understanding the components of the word2vec model -- Input of the word2vec -- Output of word2vec -- Construction components of the word2vec model -- Architectural component -- Understanding the logic of the word2vec model -- Vocabulary builder -- Context builder -- Neural network with two layers -- Structural details of a word2vec neural network -- Word2vec neural network layer's details -- Softmax function -- Main processing algorithms -- Continuous bag of words -- Skip-gram -- Understanding algorithmic techniques and the mathematics behind the word2vec model -- Understanding the basic mathematics for the word2vec algorithm -- Techniques used at the vocabulary building stage -- Lossy counting -- Using it at the stage of vocabulary building -- Applications -- Techniques used at the context building stage -- Dynamic window scaling -- Understanding dynamic context window techniques -- Subsampling -- Pruning -- Algorithms used by neural networks -- Structure of the neurons -- Basic neuron structure -- Training a simple neuron -- Define error function -- Understanding gradient descent in word2vec -- Single neuron application -- Multi-layer neural networks -- Backpropagation -- Mathematics behind the word2vec model -- Techniques used to generate final vectors and probability prediction stage -- Hierarchical softmax -- Negative sampling -- Some of the facts related to word2vec -- Applications of word2vec -- Implementation of simple examples -- Famous example (king - man + woman). | ||
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
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| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 2 | |a Natural Language Processing |0 https://id.nlm.nih.gov/mesh/D009323 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
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| contents | Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction -- Understanding natural language processing -- Understanding basic applications -- Understanding advanced applications -- Advantages of togetherness -- NLP and Python -- Environment setup for NLTK -- Tips for readers -- Summary -- Chapter 2: Practical Understanding of a Corpus and Dataset -- What is a corpus? -- Why do we need a corpus? -- Understanding corpus analysis -- Exercise -- Understanding types of data attributes -- Categorical or qualitative data attributes -- Numeric or quantitative data attributes -- Exploring different file formats for corpora -- Resources for accessing free corpora -- Preparing a dataset for NLP applications -- Selecting data -- Preprocessing the dataset -- Formatting -- Cleaning -- Sampling -- Transforming data -- Web scraping -- Summary -- Chapter 3: Understanding the Structure of a Sentences -- Understanding components of NLP -- Natural language understanding -- Natural language generation -- Differences between NLU and NLG -- Branches of NLP -- Defining context-free grammar -- Exercise -- Morphological analysis -- What is morphology? -- What are morphemes? -- What is a stem? -- What is morphological analysis? -- What is a word? -- Classification of morphemes -- Free morphemes -- Bound morphemes -- Derivational morphemes -- Inflectional morphemes -- What is the difference between a stem and a root? -- Exercise -- Lexical analysis -- What is a token? -- What are part of speech tags? -- Process of deriving tokens -- Difference between stemming and lemmatization -- Applications -- Syntactic analysis -- What is syntactic analysis? -- Semantic analysis -- What is semantic analysis? -- Lexical semantics. Hyponymy and hyponyms -- Homonymy -- Polysemy -- What is the difference between polysemy and homonymy? -- Application of semantic analysis -- Handling ambiguity -- Lexical ambiguity -- Syntactic ambiguity -- Approach to handle syntactic ambiguity -- Semantic ambiguity -- Pragmatic ambiguity -- Discourse integration -- Applications -- Pragmatic analysis -- Summary -- Chapter 4: Preprocessing -- Handling corpus-raw text -- Getting raw text -- Lowercase conversion -- Sentence tokenization -- Challenges of sentence tokenization -- Stemming for raw text -- Challenges of stemming for raw text -- Lemmatization of raw text -- Challenges of lemmatization of raw text -- Stop word removal -- Exercise -- Handling corpus-raw sentences -- Word tokenization -- Challenges for word tokenization -- Word lemmatization -- Challenges for word lemmatization -- Basic preprocessing -- Regular expressions -- Basic level regular expression -- Basic flags -- Advanced level regular expression -- Positive lookahead -- Positive lookbehind -- Negative lookahead -- Negative lookbehind -- Practical and customized preprocessing -- Decide by yourself -- Is preprocessing required? -- What kind of preprocessing is required? -- Understanding case studies of preprocessing -- Grammar correction system -- Sentiment analysis -- Machine translation -- Spelling correction -- Approach -- Summary -- Chapter 5: Feature Engineering and NLP Algorithms -- Understanding feature engineering -- What is feature engineering? -- What is the purpose of feature engineering? -- Challenges -- Basic feature of NLP -- Parsers and parsing -- Understanding the basics of parsers -- Understanding the concept of parsing -- Developing a parser from scratch -- Types of grammar -- Context-free grammar -- Probabilistic context-free grammar -- Calculating the probability of a tree. Calculating the probability of a string -- Grammar transformation -- Developing a parser with the Cocke-Kasami-Younger Algorithm -- Developing parsers step-by-step -- Existing parser tools -- The Stanford parser -- The spaCy parser -- Extracting and understanding the features -- Customizing parser tools -- Challenges -- POS tagging and POS taggers -- Understanding the concept of POS tagging and POS taggers -- Developing POS taggers step-by-step -- Plug and play with existing POS taggers -- A Stanford POS tagger example -- Using polyglot to generate POS tagging -- Exercise -- Using POS tags as features -- Challenges -- Name entity recognition -- Classes of NER -- Plug and play with existing NER tools -- A Stanford NER example -- A Spacy NER example -- Extracting and understanding the features -- Challenges -- n-grams -- Understanding n-gram using a practice example -- Application -- Bag of words -- Understanding BOW -- Understanding BOW using a practical example -- Comparing n-grams and BOW -- Applications -- Semantic tools and resources -- Basic statistical features for NLP -- Basic mathematics -- Basic concepts of linear algebra for NLP -- Basic concepts of the probabilistic theory for NLP -- Probability -- Independent event and dependent event -- Conditional probability -- TF-IDF -- Understanding TF-IDF -- Understanding TF-IDF with a practical example -- Using textblob -- Using scikit-learn -- Application -- Vectorization -- Encoders and decoders -- One-hot encoding -- Understanding a practical example for one-hot encoding -- Application -- Normalization -- The linguistics aspect of normalization -- The statistical aspect of normalization -- Probabilistic models -- Understanding probabilistic language modeling -- Application of LM -- Indexing -- Application -- Ranking -- Advantages of features engineering -- Challenges of features engineering. Advantages of word2vec -- Challenges of word2vec -- How is word2vec used in real-life applications? -- When should you use word2vec? -- Developing something interesting -- Exercise -- Extension of the word2vec concept -- Para2Vec -- Doc2Vec -- Applications of Doc2vec -- GloVe -- Exercise -- Importance of vectorization in deep learning -- Summary -- Chapter 7: Rule-Based System for NLP -- Understanding of the rule-based system -- What does the RB system mean? -- Purpose of having the rule-based system -- Why do we need the rule-based system? -- Which kind of applications can use the RB approach over the other approaches? -- Exercise -- What kind of resources do you need if you want to develop a rule-based system? -- Architecture of the RB system -- General architecture of the rule-based system as an expert system -- Practical architecture of the rule-based system for NLP applications -- Custom architecture -- the RB system for NLP applications -- Exercise -- Apache UIMA -- the RB system for NLP applications -- Understanding the RB system development life cycle -- Applications -- NLP applications using the rule-based system -- Generalized AI applications using the rule-based system -- Developing NLP applications using the RB system -- Thinking process for making rules -- Start with simple rules -- Scraping the text data -- Defining the rule for our goal -- Coding our rule and generating a prototype and result -- Exercise -- Python for pattern-matching rules for a proofreading application -- Exercise -- Grammar correction -- Template-based chatbot application -- Flow of code -- Advantages of template-based chatbot -- Disadvantages of template-based chatbot -- Exercise -- Comparing the rule-based approach with other approaches -- Advantages of the rule-based system -- Disadvantages of the rule-based system -- Challenges for the rule-based system. |
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title from title page (Safari, viewed August 18, 2017).</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction -- Understanding natural language processing -- Understanding basic applications -- Understanding advanced applications -- Advantages of togetherness -- NLP and Python -- Environment setup for NLTK -- Tips for readers -- Summary -- Chapter 2: Practical Understanding of a Corpus and Dataset -- What is a corpus? -- Why do we need a corpus? -- Understanding corpus analysis -- Exercise -- Understanding types of data attributes -- Categorical or qualitative data attributes -- Numeric or quantitative data attributes -- Exploring different file formats for corpora -- Resources for accessing free corpora -- Preparing a dataset for NLP applications -- Selecting data -- Preprocessing the dataset -- Formatting -- Cleaning -- Sampling -- Transforming data -- Web scraping -- Summary -- Chapter 3: Understanding the Structure of a Sentences -- Understanding components of NLP -- Natural language understanding -- Natural language generation -- Differences between NLU and NLG -- Branches of NLP -- Defining context-free grammar -- Exercise -- Morphological analysis -- What is morphology? -- What are morphemes? -- What is a stem? -- What is morphological analysis? -- What is a word? -- Classification of morphemes -- Free morphemes -- Bound morphemes -- Derivational morphemes -- Inflectional morphemes -- What is the difference between a stem and a root? -- Exercise -- Lexical analysis -- What is a token? -- What are part of speech tags? -- Process of deriving tokens -- Difference between stemming and lemmatization -- Applications -- Syntactic analysis -- What is syntactic analysis? -- Semantic analysis -- What is semantic analysis? -- Lexical semantics.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Hyponymy and hyponyms -- Homonymy -- Polysemy -- What is the difference between polysemy and homonymy? -- Application of semantic analysis -- Handling ambiguity -- Lexical ambiguity -- Syntactic ambiguity -- Approach to handle syntactic ambiguity -- Semantic ambiguity -- Pragmatic ambiguity -- Discourse integration -- Applications -- Pragmatic analysis -- Summary -- Chapter 4: Preprocessing -- Handling corpus-raw text -- Getting raw text -- Lowercase conversion -- Sentence tokenization -- Challenges of sentence tokenization -- Stemming for raw text -- Challenges of stemming for raw text -- Lemmatization of raw text -- Challenges of lemmatization of raw text -- Stop word removal -- Exercise -- Handling corpus-raw sentences -- Word tokenization -- Challenges for word tokenization -- Word lemmatization -- Challenges for word lemmatization -- Basic preprocessing -- Regular expressions -- Basic level regular expression -- Basic flags -- Advanced level regular expression -- Positive lookahead -- Positive lookbehind -- Negative lookahead -- Negative lookbehind -- Practical and customized preprocessing -- Decide by yourself -- Is preprocessing required? -- What kind of preprocessing is required? -- Understanding case studies of preprocessing -- Grammar correction system -- Sentiment analysis -- Machine translation -- Spelling correction -- Approach -- Summary -- Chapter 5: Feature Engineering and NLP Algorithms -- Understanding feature engineering -- What is feature engineering? -- What is the purpose of feature engineering? -- Challenges -- Basic feature of NLP -- Parsers and parsing -- Understanding the basics of parsers -- Understanding the concept of parsing -- Developing a parser from scratch -- Types of grammar -- Context-free grammar -- Probabilistic context-free grammar -- Calculating the probability of a tree.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Calculating the probability of a string -- Grammar transformation -- Developing a parser with the Cocke-Kasami-Younger Algorithm -- Developing parsers step-by-step -- Existing parser tools -- The Stanford parser -- The spaCy parser -- Extracting and understanding the features -- Customizing parser tools -- Challenges -- POS tagging and POS taggers -- Understanding the concept of POS tagging and POS taggers -- Developing POS taggers step-by-step -- Plug and play with existing POS taggers -- A Stanford POS tagger example -- Using polyglot to generate POS tagging -- Exercise -- Using POS tags as features -- Challenges -- Name entity recognition -- Classes of NER -- Plug and play with existing NER tools -- A Stanford NER example -- A Spacy NER example -- Extracting and understanding the features -- Challenges -- n-grams -- Understanding n-gram using a practice example -- Application -- Bag of words -- Understanding BOW -- Understanding BOW using a practical example -- Comparing n-grams and BOW -- Applications -- Semantic tools and resources -- Basic statistical features for NLP -- Basic mathematics -- Basic concepts of linear algebra for NLP -- Basic concepts of the probabilistic theory for NLP -- Probability -- Independent event and dependent event -- Conditional probability -- TF-IDF -- Understanding TF-IDF -- Understanding TF-IDF with a practical example -- Using textblob -- Using scikit-learn -- Application -- Vectorization -- Encoders and decoders -- One-hot encoding -- Understanding a practical example for one-hot encoding -- Application -- Normalization -- The linguistics aspect of normalization -- The statistical aspect of normalization -- Probabilistic models -- Understanding probabilistic language modeling -- Application of LM -- Indexing -- Application -- Ranking -- Advantages of features engineering -- Challenges of features engineering.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Advantages of word2vec -- Challenges of word2vec -- How is word2vec used in real-life applications? -- When should you use word2vec? -- Developing something interesting -- Exercise -- Extension of the word2vec concept -- Para2Vec -- Doc2Vec -- Applications of Doc2vec -- GloVe -- Exercise -- Importance of vectorization in deep learning -- Summary -- Chapter 7: Rule-Based System for NLP -- Understanding of the rule-based system -- What does the RB system mean? -- Purpose of having the rule-based system -- Why do we need the rule-based system? -- Which kind of applications can use the RB approach over the other approaches? -- Exercise -- What kind of resources do you need if you want to develop a rule-based system? -- Architecture of the RB system -- General architecture of the rule-based system as an expert system -- Practical architecture of the rule-based system for NLP applications -- Custom architecture -- the RB system for NLP applications -- Exercise -- Apache UIMA -- the RB system for NLP applications -- Understanding the RB system development life cycle -- Applications -- NLP applications using the rule-based system -- Generalized AI applications using the rule-based system -- Developing NLP applications using the RB system -- Thinking process for making rules -- Start with simple rules -- Scraping the text data -- Defining the rule for our goal -- Coding our rule and generating a prototype and result -- Exercise -- Python for pattern-matching rules for a proofreading application -- Exercise -- Grammar correction -- Template-based chatbot application -- Flow of code -- Advantages of template-based chatbot -- Disadvantages of template-based chatbot -- Exercise -- Comparing the rule-based approach with other approaches -- Advantages of the rule-based system -- Disadvantages of the rule-based system -- Challenges for the rule-based system.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Chapter 6: Advanced Feature Engineering and NLP 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| id | ZDB-4-EBA-on1001347182 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:27:58Z |
| institution | BVB |
| isbn | 9781787285521 1787285529 9781523112173 1523112174 |
| language | English |
| oclc_num | 1001347182 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (1 volume) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Thanaki, Jalaj, author. Python natural language processing : explore NLP with machine learning and deep learning techniques / Jalaj Thanaki. Birmingham, UK : Packt Publishing, 2017. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed August 18, 2017). Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction -- Understanding natural language processing -- Understanding basic applications -- Understanding advanced applications -- Advantages of togetherness -- NLP and Python -- Environment setup for NLTK -- Tips for readers -- Summary -- Chapter 2: Practical Understanding of a Corpus and Dataset -- What is a corpus? -- Why do we need a corpus? -- Understanding corpus analysis -- Exercise -- Understanding types of data attributes -- Categorical or qualitative data attributes -- Numeric or quantitative data attributes -- Exploring different file formats for corpora -- Resources for accessing free corpora -- Preparing a dataset for NLP applications -- Selecting data -- Preprocessing the dataset -- Formatting -- Cleaning -- Sampling -- Transforming data -- Web scraping -- Summary -- Chapter 3: Understanding the Structure of a Sentences -- Understanding components of NLP -- Natural language understanding -- Natural language generation -- Differences between NLU and NLG -- Branches of NLP -- Defining context-free grammar -- Exercise -- Morphological analysis -- What is morphology? -- What are morphemes? -- What is a stem? -- What is morphological analysis? -- What is a word? -- Classification of morphemes -- Free morphemes -- Bound morphemes -- Derivational morphemes -- Inflectional morphemes -- What is the difference between a stem and a root? -- Exercise -- Lexical analysis -- What is a token? -- What are part of speech tags? -- Process of deriving tokens -- Difference between stemming and lemmatization -- Applications -- Syntactic analysis -- What is syntactic analysis? -- Semantic analysis -- What is semantic analysis? -- Lexical semantics. Hyponymy and hyponyms -- Homonymy -- Polysemy -- What is the difference between polysemy and homonymy? -- Application of semantic analysis -- Handling ambiguity -- Lexical ambiguity -- Syntactic ambiguity -- Approach to handle syntactic ambiguity -- Semantic ambiguity -- Pragmatic ambiguity -- Discourse integration -- Applications -- Pragmatic analysis -- Summary -- Chapter 4: Preprocessing -- Handling corpus-raw text -- Getting raw text -- Lowercase conversion -- Sentence tokenization -- Challenges of sentence tokenization -- Stemming for raw text -- Challenges of stemming for raw text -- Lemmatization of raw text -- Challenges of lemmatization of raw text -- Stop word removal -- Exercise -- Handling corpus-raw sentences -- Word tokenization -- Challenges for word tokenization -- Word lemmatization -- Challenges for word lemmatization -- Basic preprocessing -- Regular expressions -- Basic level regular expression -- Basic flags -- Advanced level regular expression -- Positive lookahead -- Positive lookbehind -- Negative lookahead -- Negative lookbehind -- Practical and customized preprocessing -- Decide by yourself -- Is preprocessing required? -- What kind of preprocessing is required? -- Understanding case studies of preprocessing -- Grammar correction system -- Sentiment analysis -- Machine translation -- Spelling correction -- Approach -- Summary -- Chapter 5: Feature Engineering and NLP Algorithms -- Understanding feature engineering -- What is feature engineering? -- What is the purpose of feature engineering? -- Challenges -- Basic feature of NLP -- Parsers and parsing -- Understanding the basics of parsers -- Understanding the concept of parsing -- Developing a parser from scratch -- Types of grammar -- Context-free grammar -- Probabilistic context-free grammar -- Calculating the probability of a tree. Calculating the probability of a string -- Grammar transformation -- Developing a parser with the Cocke-Kasami-Younger Algorithm -- Developing parsers step-by-step -- Existing parser tools -- The Stanford parser -- The spaCy parser -- Extracting and understanding the features -- Customizing parser tools -- Challenges -- POS tagging and POS taggers -- Understanding the concept of POS tagging and POS taggers -- Developing POS taggers step-by-step -- Plug and play with existing POS taggers -- A Stanford POS tagger example -- Using polyglot to generate POS tagging -- Exercise -- Using POS tags as features -- Challenges -- Name entity recognition -- Classes of NER -- Plug and play with existing NER tools -- A Stanford NER example -- A Spacy NER example -- Extracting and understanding the features -- Challenges -- n-grams -- Understanding n-gram using a practice example -- Application -- Bag of words -- Understanding BOW -- Understanding BOW using a practical example -- Comparing n-grams and BOW -- Applications -- Semantic tools and resources -- Basic statistical features for NLP -- Basic mathematics -- Basic concepts of linear algebra for NLP -- Basic concepts of the probabilistic theory for NLP -- Probability -- Independent event and dependent event -- Conditional probability -- TF-IDF -- Understanding TF-IDF -- Understanding TF-IDF with a practical example -- Using textblob -- Using scikit-learn -- Application -- Vectorization -- Encoders and decoders -- One-hot encoding -- Understanding a practical example for one-hot encoding -- Application -- Normalization -- The linguistics aspect of normalization -- The statistical aspect of normalization -- Probabilistic models -- Understanding probabilistic language modeling -- Application of LM -- Indexing -- Application -- Ranking -- Advantages of features engineering -- Challenges of features engineering. Advantages of word2vec -- Challenges of word2vec -- How is word2vec used in real-life applications? -- When should you use word2vec? -- Developing something interesting -- Exercise -- Extension of the word2vec concept -- Para2Vec -- Doc2Vec -- Applications of Doc2vec -- GloVe -- Exercise -- Importance of vectorization in deep learning -- Summary -- Chapter 7: Rule-Based System for NLP -- Understanding of the rule-based system -- What does the RB system mean? -- Purpose of having the rule-based system -- Why do we need the rule-based system? -- Which kind of applications can use the RB approach over the other approaches? -- Exercise -- What kind of resources do you need if you want to develop a rule-based system? -- Architecture of the RB system -- General architecture of the rule-based system as an expert system -- Practical architecture of the rule-based system for NLP applications -- Custom architecture -- the RB system for NLP applications -- Exercise -- Apache UIMA -- the RB system for NLP applications -- Understanding the RB system development life cycle -- Applications -- NLP applications using the rule-based system -- Generalized AI applications using the rule-based system -- Developing NLP applications using the RB system -- Thinking process for making rules -- Start with simple rules -- Scraping the text data -- Defining the rule for our goal -- Coding our rule and generating a prototype and result -- Exercise -- Python for pattern-matching rules for a proofreading application -- Exercise -- Grammar correction -- Template-based chatbot application -- Flow of code -- Advantages of template-based chatbot -- Disadvantages of template-based chatbot -- Exercise -- Comparing the rule-based approach with other approaches -- Advantages of the rule-based system -- Disadvantages of the rule-based system -- Challenges for the rule-based system. Chapter 6: Advanced Feature Engineering and NLP Algorithms -- Recall word embedding -- Understanding the basics of word2vec -- Distributional semantics -- Defining word2vec -- Necessity of unsupervised distribution semantic model - word2vec -- Challenges -- Converting the word2vec model from black box to white box -- Distributional similarity based representation -- Understanding the components of the word2vec model -- Input of the word2vec -- Output of word2vec -- Construction components of the word2vec model -- Architectural component -- Understanding the logic of the word2vec model -- Vocabulary builder -- Context builder -- Neural network with two layers -- Structural details of a word2vec neural network -- Word2vec neural network layer's details -- Softmax function -- Main processing algorithms -- Continuous bag of words -- Skip-gram -- Understanding algorithmic techniques and the mathematics behind the word2vec model -- Understanding the basic mathematics for the word2vec algorithm -- Techniques used at the vocabulary building stage -- Lossy counting -- Using it at the stage of vocabulary building -- Applications -- Techniques used at the context building stage -- Dynamic window scaling -- Understanding dynamic context window techniques -- Subsampling -- Pruning -- Algorithms used by neural networks -- Structure of the neurons -- Basic neuron structure -- Training a simple neuron -- Define error function -- Understanding gradient descent in word2vec -- Single neuron application -- Multi-layer neural networks -- Backpropagation -- Mathematics behind the word2vec model -- Techniques used to generate final vectors and probability prediction stage -- Hierarchical softmax -- Negative sampling -- Some of the facts related to word2vec -- Applications of word2vec -- Implementation of simple examples -- Famous example (king - man + woman). Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Traitement automatique des langues naturelles. Apprentissage automatique. COMPUTERS Natural Language Processing. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Programming Languages General. bisacsh Machine learning fast Natural language processing (Computer science) fast Python (Computer program language) fast FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1566414 Volltext |
| spellingShingle | Thanaki, Jalaj Python natural language processing : explore NLP with machine learning and deep learning techniques / Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction -- Understanding natural language processing -- Understanding basic applications -- Understanding advanced applications -- Advantages of togetherness -- NLP and Python -- Environment setup for NLTK -- Tips for readers -- Summary -- Chapter 2: Practical Understanding of a Corpus and Dataset -- What is a corpus? -- Why do we need a corpus? -- Understanding corpus analysis -- Exercise -- Understanding types of data attributes -- Categorical or qualitative data attributes -- Numeric or quantitative data attributes -- Exploring different file formats for corpora -- Resources for accessing free corpora -- Preparing a dataset for NLP applications -- Selecting data -- Preprocessing the dataset -- Formatting -- Cleaning -- Sampling -- Transforming data -- Web scraping -- Summary -- Chapter 3: Understanding the Structure of a Sentences -- Understanding components of NLP -- Natural language understanding -- Natural language generation -- Differences between NLU and NLG -- Branches of NLP -- Defining context-free grammar -- Exercise -- Morphological analysis -- What is morphology? -- What are morphemes? -- What is a stem? -- What is morphological analysis? -- What is a word? -- Classification of morphemes -- Free morphemes -- Bound morphemes -- Derivational morphemes -- Inflectional morphemes -- What is the difference between a stem and a root? -- Exercise -- Lexical analysis -- What is a token? -- What are part of speech tags? -- Process of deriving tokens -- Difference between stemming and lemmatization -- Applications -- Syntactic analysis -- What is syntactic analysis? -- Semantic analysis -- What is semantic analysis? -- Lexical semantics. Hyponymy and hyponyms -- Homonymy -- Polysemy -- What is the difference between polysemy and homonymy? -- Application of semantic analysis -- Handling ambiguity -- Lexical ambiguity -- Syntactic ambiguity -- Approach to handle syntactic ambiguity -- Semantic ambiguity -- Pragmatic ambiguity -- Discourse integration -- Applications -- Pragmatic analysis -- Summary -- Chapter 4: Preprocessing -- Handling corpus-raw text -- Getting raw text -- Lowercase conversion -- Sentence tokenization -- Challenges of sentence tokenization -- Stemming for raw text -- Challenges of stemming for raw text -- Lemmatization of raw text -- Challenges of lemmatization of raw text -- Stop word removal -- Exercise -- Handling corpus-raw sentences -- Word tokenization -- Challenges for word tokenization -- Word lemmatization -- Challenges for word lemmatization -- Basic preprocessing -- Regular expressions -- Basic level regular expression -- Basic flags -- Advanced level regular expression -- Positive lookahead -- Positive lookbehind -- Negative lookahead -- Negative lookbehind -- Practical and customized preprocessing -- Decide by yourself -- Is preprocessing required? -- What kind of preprocessing is required? -- Understanding case studies of preprocessing -- Grammar correction system -- Sentiment analysis -- Machine translation -- Spelling correction -- Approach -- Summary -- Chapter 5: Feature Engineering and NLP Algorithms -- Understanding feature engineering -- What is feature engineering? -- What is the purpose of feature engineering? -- Challenges -- Basic feature of NLP -- Parsers and parsing -- Understanding the basics of parsers -- Understanding the concept of parsing -- Developing a parser from scratch -- Types of grammar -- Context-free grammar -- Probabilistic context-free grammar -- Calculating the probability of a tree. Calculating the probability of a string -- Grammar transformation -- Developing a parser with the Cocke-Kasami-Younger Algorithm -- Developing parsers step-by-step -- Existing parser tools -- The Stanford parser -- The spaCy parser -- Extracting and understanding the features -- Customizing parser tools -- Challenges -- POS tagging and POS taggers -- Understanding the concept of POS tagging and POS taggers -- Developing POS taggers step-by-step -- Plug and play with existing POS taggers -- A Stanford POS tagger example -- Using polyglot to generate POS tagging -- Exercise -- Using POS tags as features -- Challenges -- Name entity recognition -- Classes of NER -- Plug and play with existing NER tools -- A Stanford NER example -- A Spacy NER example -- Extracting and understanding the features -- Challenges -- n-grams -- Understanding n-gram using a practice example -- Application -- Bag of words -- Understanding BOW -- Understanding BOW using a practical example -- Comparing n-grams and BOW -- Applications -- Semantic tools and resources -- Basic statistical features for NLP -- Basic mathematics -- Basic concepts of linear algebra for NLP -- Basic concepts of the probabilistic theory for NLP -- Probability -- Independent event and dependent event -- Conditional probability -- TF-IDF -- Understanding TF-IDF -- Understanding TF-IDF with a practical example -- Using textblob -- Using scikit-learn -- Application -- Vectorization -- Encoders and decoders -- One-hot encoding -- Understanding a practical example for one-hot encoding -- Application -- Normalization -- The linguistics aspect of normalization -- The statistical aspect of normalization -- Probabilistic models -- Understanding probabilistic language modeling -- Application of LM -- Indexing -- Application -- Ranking -- Advantages of features engineering -- Challenges of features engineering. Advantages of word2vec -- Challenges of word2vec -- How is word2vec used in real-life applications? -- When should you use word2vec? -- Developing something interesting -- Exercise -- Extension of the word2vec concept -- Para2Vec -- Doc2Vec -- Applications of Doc2vec -- GloVe -- Exercise -- Importance of vectorization in deep learning -- Summary -- Chapter 7: Rule-Based System for NLP -- Understanding of the rule-based system -- What does the RB system mean? -- Purpose of having the rule-based system -- Why do we need the rule-based system? -- Which kind of applications can use the RB approach over the other approaches? -- Exercise -- What kind of resources do you need if you want to develop a rule-based system? -- Architecture of the RB system -- General architecture of the rule-based system as an expert system -- Practical architecture of the rule-based system for NLP applications -- Custom architecture -- the RB system for NLP applications -- Exercise -- Apache UIMA -- the RB system for NLP applications -- Understanding the RB system development life cycle -- Applications -- NLP applications using the rule-based system -- Generalized AI applications using the rule-based system -- Developing NLP applications using the RB system -- Thinking process for making rules -- Start with simple rules -- Scraping the text data -- Defining the rule for our goal -- Coding our rule and generating a prototype and result -- Exercise -- Python for pattern-matching rules for a proofreading application -- Exercise -- Grammar correction -- Template-based chatbot application -- Flow of code -- Advantages of template-based chatbot -- Disadvantages of template-based chatbot -- Exercise -- Comparing the rule-based approach with other approaches -- Advantages of the rule-based system -- Disadvantages of the rule-based system -- Challenges for the rule-based system. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Traitement automatique des langues naturelles. Apprentissage automatique. COMPUTERS Natural Language Processing. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Programming Languages General. bisacsh Machine learning fast Natural language processing (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh88002425 http://id.loc.gov/authorities/subjects/sh85079324 https://id.nlm.nih.gov/mesh/D009323 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Python natural language processing : explore NLP with machine learning and deep learning techniques / |
| title_auth | Python natural language processing : explore NLP with machine learning and deep learning techniques / |
| title_exact_search | Python natural language processing : explore NLP with machine learning and deep learning techniques / |
| title_full | Python natural language processing : explore NLP with machine learning and deep learning techniques / Jalaj Thanaki. |
| title_fullStr | Python natural language processing : explore NLP with machine learning and deep learning techniques / Jalaj Thanaki. |
| title_full_unstemmed | Python natural language processing : explore NLP with machine learning and deep learning techniques / Jalaj Thanaki. |
| title_short | Python natural language processing : |
| title_sort | python natural language processing explore nlp with machine learning and deep learning techniques |
| title_sub | explore NLP with machine learning and deep learning techniques / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Traitement automatique des langues naturelles. Apprentissage automatique. COMPUTERS Natural Language Processing. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Programming Languages General. bisacsh Machine learning fast Natural language processing (Computer science) fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Natural language processing (Computer science) Machine learning. Natural Language Processing Machine Learning Python (Langage de programmation) Traitement automatique des langues naturelles. Apprentissage automatique. COMPUTERS Natural Language Processing. COMPUTERS Data Processing. COMPUTERS Programming Languages General. Machine learning |
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| work_keys_str_mv | AT thanakijalaj pythonnaturallanguageprocessingexplorenlpwithmachinelearninganddeeplearningtechniques |