Python Natural Language Processing Cookbook: Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks.

Antić  Zhenya.

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dc.contributor.author	Antić Zhenya.
dc.date.accessioned	2024-01-26T21:36:38Z
dc.date.available	2024-01-26T21:36:38Z
dc.date.issued	2021
dc.identifier.citation	Antić. 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.) - URL: https://libweb.kpfu.ru/ebsco/pdf/2894698.pdf
dc.identifier.isbn	1838987789
dc.identifier.isbn	9781838987787
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/178468
dc.description	Description based upon print version of record.
dc.description	How to do it...
dc.description.abstract	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.
dc.description.tableofcontents	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
dc.description.tableofcontents	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
dc.description.tableofcontents	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...
dc.description.tableofcontents	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...
dc.description.tableofcontents	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
dc.language	English
dc.language.iso	en
dc.publisher	Birmingham Packt Publishing, Limited
dc.subject.other	Natural language processing (Computer science)
dc.subject.other	Python (Computer program language)
dc.subject.other	Natural Language Processing
dc.subject.other	Traitement automatique des langues naturelles.
dc.subject.other	Python (Langage de programmation)
dc.subject.other	Natural language processing (Computer science)
dc.subject.other	Python (Computer program language)
dc.title	Python Natural Language Processing Cookbook: Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks.
dc.type	Book
dc.description.pages	1 online resource (285 p.)
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C70301