Python Natural Language Processing Cookbook :: Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks.
Leverage your natural language processing skills to make sense of text. With this book, you'll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You'll also find recipes for overcoming common ch...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | Leverage your natural language processing skills to make sense of text. With this book, you'll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You'll also find recipes for overcoming common challenges in implementing NLP pipelines. |
| Beschreibung: | How to do it... |
| Beschreibung: | 1 online resource (285 p.) |
| ISBN: | 9781838987787 1838987789 1838987312 9781838987312 |
Internformat
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| 100 | 1 | |a Antic, Zhenya. | |
| 245 | 1 | 0 | |a Python Natural Language Processing Cookbook : |b Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, Limited, |c 2021. | |
| 300 | |a 1 online resource (285 p.) | ||
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Learning NLP Basics -- Technical requirements -- Dividing text into sentences -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Dividing sentences into words -- tokenization -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Parts of speech tagging -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Word stemming -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also | |
| 505 | 8 | |a Combining similar words -- lemmatization -- Getting ready -- How to do it... -- How it works... -- There's more... -- Removing stopwords -- Getting ready... -- How to do it... -- How it works... -- There's more... -- Chapter 2: Playing with Grammar -- Technical requirements -- Counting nouns -- plural and singular nouns -- Getting ready -- How to do it... -- How it works... -- There's more... -- Getting the dependency parse -- Getting ready -- How to do it... -- How it works... -- See also -- Splitting sentences into clauses -- Getting ready -- How to do it... -- How it works... -- Extracting noun chunks -- Getting ready | |
| 505 | 8 | |a How to do it... -- How it works... -- There's more... -- See also -- Extracting entities and relations -- Getting ready -- How to do it... -- How it works... -- There's more... -- Extracting subjects and objects of the sentence -- Getting ready -- How to do it... -- How it works... -- There's more... -- Finding references -- anaphora resolution -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 3: Representing Text -- Capturing Semantics -- Technical requirements -- Putting documents into a bag of words -- Getting ready -- How to do it... -- How it works... -- There's more... | |
| 505 | 8 | |a Constructing the N-gram model -- Getting ready -- How to do it... -- How it works... -- There's more... -- Representing texts with TF-IDF -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using word embeddings -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Training your own embeddings model -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Representing phrases -- phrase2vec -- Getting ready -- How to do it... -- How it works... -- See also -- Using BERT instead of word embeddings -- Getting ready -- How to do it... | |
| 505 | 8 | |a How it works... -- Getting started with semantic search -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 4: Classifying Texts -- Technical requirements -- Getting the dataset and evaluation baseline ready -- Getting ready -- How to do it... -- How it works... -- Performing rule-based text classification using keywords -- Getting ready -- How to do it... -- How it works... -- There's more... -- Clustering sentences using K-means -- unsupervised text classification -- Getting ready -- How to do it... -- How it works... -- Using SVMs for supervised text classification -- Getting ready | |
| 500 | |a How to do it... | ||
| 520 | |a Leverage your natural language processing skills to make sense of text. With this book, you'll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You'll also find recipes for overcoming common challenges in implementing NLP pipelines. | ||
| 650 | 0 | |a Natural language processing (Computer science) |0 http://id.loc.gov/authorities/subjects/sh88002425 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 2 | |a Natural Language Processing |0 https://id.nlm.nih.gov/mesh/D009323 | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Natural language processing (Computer science) |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
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| contents | Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Learning NLP Basics -- Technical requirements -- Dividing text into sentences -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Dividing sentences into words -- tokenization -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Parts of speech tagging -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Word stemming -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also Combining similar words -- lemmatization -- Getting ready -- How to do it... -- How it works... -- There's more... -- Removing stopwords -- Getting ready... -- How to do it... -- How it works... -- There's more... -- Chapter 2: Playing with Grammar -- Technical requirements -- Counting nouns -- plural and singular nouns -- Getting ready -- How to do it... -- How it works... -- There's more... -- Getting the dependency parse -- Getting ready -- How to do it... -- How it works... -- See also -- Splitting sentences into clauses -- Getting ready -- How to do it... -- How it works... -- Extracting noun chunks -- Getting ready How to do it... -- How it works... -- There's more... -- See also -- Extracting entities and relations -- Getting ready -- How to do it... -- How it works... -- There's more... -- Extracting subjects and objects of the sentence -- Getting ready -- How to do it... -- How it works... -- There's more... -- Finding references -- anaphora resolution -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 3: Representing Text -- Capturing Semantics -- Technical requirements -- Putting documents into a bag of words -- Getting ready -- How to do it... -- How it works... -- There's more... Constructing the N-gram model -- Getting ready -- How to do it... -- How it works... -- There's more... -- Representing texts with TF-IDF -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using word embeddings -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Training your own embeddings model -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Representing phrases -- phrase2vec -- Getting ready -- How to do it... -- How it works... -- See also -- Using BERT instead of word embeddings -- Getting ready -- How to do it... How it works... -- Getting started with semantic search -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 4: Classifying Texts -- Technical requirements -- Getting the dataset and evaluation baseline ready -- Getting ready -- How to do it... -- How it works... -- Performing rule-based text classification using keywords -- Getting ready -- How to do it... -- How it works... -- There's more... -- Clustering sentences using K-means -- unsupervised text classification -- Getting ready -- How to do it... -- How it works... -- Using SVMs for supervised text classification -- Getting ready |
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| publisher | Packt Publishing, Limited, |
| record_format | marc |
| spelling | Antic, Zhenya. Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. Birmingham : Packt Publishing, Limited, 2021. 1 online resource (285 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Learning NLP Basics -- Technical requirements -- Dividing text into sentences -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Dividing sentences into words -- tokenization -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Parts of speech tagging -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Word stemming -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also Combining similar words -- lemmatization -- Getting ready -- How to do it... -- How it works... -- There's more... -- Removing stopwords -- Getting ready... -- How to do it... -- How it works... -- There's more... -- Chapter 2: Playing with Grammar -- Technical requirements -- Counting nouns -- plural and singular nouns -- Getting ready -- How to do it... -- How it works... -- There's more... -- Getting the dependency parse -- Getting ready -- How to do it... -- How it works... -- See also -- Splitting sentences into clauses -- Getting ready -- How to do it... -- How it works... -- Extracting noun chunks -- Getting ready How to do it... -- How it works... -- There's more... -- See also -- Extracting entities and relations -- Getting ready -- How to do it... -- How it works... -- There's more... -- Extracting subjects and objects of the sentence -- Getting ready -- How to do it... -- How it works... -- There's more... -- Finding references -- anaphora resolution -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 3: Representing Text -- Capturing Semantics -- Technical requirements -- Putting documents into a bag of words -- Getting ready -- How to do it... -- How it works... -- There's more... Constructing the N-gram model -- Getting ready -- How to do it... -- How it works... -- There's more... -- Representing texts with TF-IDF -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using word embeddings -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Training your own embeddings model -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Representing phrases -- phrase2vec -- Getting ready -- How to do it... -- How it works... -- See also -- Using BERT instead of word embeddings -- Getting ready -- How to do it... How it works... -- Getting started with semantic search -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 4: Classifying Texts -- Technical requirements -- Getting the dataset and evaluation baseline ready -- Getting ready -- How to do it... -- How it works... -- Performing rule-based text classification using keywords -- Getting ready -- How to do it... -- How it works... -- There's more... -- Clustering sentences using K-means -- unsupervised text classification -- Getting ready -- How to do it... -- How it works... -- Using SVMs for supervised text classification -- Getting ready How to do it... Leverage your natural language processing skills to make sense of text. With this book, you'll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You'll also find recipes for overcoming common challenges in implementing NLP pipelines. Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Traitement automatique des langues naturelles. Python (Langage de programmation) Natural language processing (Computer science) fast Python (Computer program language) fast has work: Python natural language processing cookbook (Text) https://id.oclc.org/worldcat/entity/E39PCFv3DbH4cyxMHQX34kfvf3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Antic, Zhenya Python Natural Language Processing Cookbook Birmingham : Packt Publishing, Limited,c2021 9781838987312 |
| spellingShingle | Antic, Zhenya Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Learning NLP Basics -- Technical requirements -- Dividing text into sentences -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Dividing sentences into words -- tokenization -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Parts of speech tagging -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Word stemming -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also Combining similar words -- lemmatization -- Getting ready -- How to do it... -- How it works... -- There's more... -- Removing stopwords -- Getting ready... -- How to do it... -- How it works... -- There's more... -- Chapter 2: Playing with Grammar -- Technical requirements -- Counting nouns -- plural and singular nouns -- Getting ready -- How to do it... -- How it works... -- There's more... -- Getting the dependency parse -- Getting ready -- How to do it... -- How it works... -- See also -- Splitting sentences into clauses -- Getting ready -- How to do it... -- How it works... -- Extracting noun chunks -- Getting ready How to do it... -- How it works... -- There's more... -- See also -- Extracting entities and relations -- Getting ready -- How to do it... -- How it works... -- There's more... -- Extracting subjects and objects of the sentence -- Getting ready -- How to do it... -- How it works... -- There's more... -- Finding references -- anaphora resolution -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 3: Representing Text -- Capturing Semantics -- Technical requirements -- Putting documents into a bag of words -- Getting ready -- How to do it... -- How it works... -- There's more... Constructing the N-gram model -- Getting ready -- How to do it... -- How it works... -- There's more... -- Representing texts with TF-IDF -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using word embeddings -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Training your own embeddings model -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Representing phrases -- phrase2vec -- Getting ready -- How to do it... -- How it works... -- See also -- Using BERT instead of word embeddings -- Getting ready -- How to do it... How it works... -- Getting started with semantic search -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 4: Classifying Texts -- Technical requirements -- Getting the dataset and evaluation baseline ready -- Getting ready -- How to do it... -- How it works... -- Performing rule-based text classification using keywords -- Getting ready -- How to do it... -- How it works... -- There's more... -- Clustering sentences using K-means -- unsupervised text classification -- Getting ready -- How to do it... -- How it works... -- Using SVMs for supervised text classification -- Getting ready Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Traitement automatique des langues naturelles. Python (Langage de programmation) Natural language processing (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh88002425 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D009323 |
| title | Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| title_auth | Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| title_exact_search | Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| title_full | Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| title_fullStr | Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| title_full_unstemmed | Python Natural Language Processing Cookbook : Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| title_short | Python Natural Language Processing Cookbook : |
| title_sort | python natural language processing cookbook over 50 recipes to understand analyze and generate text for implementing language processing tasks |
| title_sub | Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks. |
| topic | Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Traitement automatique des langues naturelles. Python (Langage de programmation) Natural language processing (Computer science) fast Python (Computer program language) fast |
| topic_facet | Natural language processing (Computer science) Python (Computer program language) Natural Language Processing Traitement automatique des langues naturelles. Python (Langage de programmation) |
| work_keys_str_mv | AT anticzhenya pythonnaturallanguageprocessingcookbookover50recipestounderstandanalyzeandgeneratetextforimplementinglanguageprocessingtasks |