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211025 ||| eng |
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|a 9781484273517
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| 050 |
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4 |
|a QA76.9.N38
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| 100 |
1 |
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|a Kulkarni, Akshay
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| 245 |
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|a Natural language processing recipes
|b unlocking text data with machine learning and deep learning using Python
|c Akshay Kulkarni, Adarsha Shivananda
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| 250 |
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|a 2nd ed
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| 260 |
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|a Berkeley, CA
|b Apress
|c 2021
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| 300 |
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|a 302 pages
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| 505 |
0 |
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|a Step 8-2. Import the libraries -- Step 8-3. Identify the URL to extract the data -- Step 8-4. Request the URL and download the content using Beautiful Soup -- Step 8-5. Understand the website's structure to extract the required information -- Step 8-6. Use Beautiful Soup to extract and parse the data from HTML tags -- Step 8-7. Convert lists to a data frame and perform an analysis that meets business requirements -- Step 8-8. Download the data frame -- Chapter 2: Exploring and Processing Text Data -- Recipe 2-1. Converting Text Data to Lowercase -- Problem -- Solution -- How It Works
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| 505 |
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|a Step 1-1. Read/create the text data -- Step 1-2. Execute the lower() function on the text data -- Recipe 2-2. Removing Punctuation -- Problem -- Solution -- How It Works -- Step 2-1. Read/create the text data -- Step 2-2. Execute the replace() function on the text data -- Recipe 2-3. Removing Stop Words -- Problem -- Solution -- How It Works -- Step 3-1. Read/create the text data -- Step 3-2. Remove punctuation from the text data -- Recipe 2-4. Standardizing Text -- Problem -- Solution -- How It Works -- Step 4-1. Create a custom lookup dictionary
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|a Step 5-5. Extract all instances of a particular tag -- Step 5-6. Extract all text from a particular tag -- Recipe 1-6. Parsing Text Using Regular Expressions -- Problem -- Solution -- How It Works -- Tokenizing -- Extracting Email IDs -- Replacing Email IDs -- Extracting Data from an eBook and Performing regex -- Recipe 1-7. Handling Strings -- Problem -- Solution -- How It Works -- Replacing Content -- Concatenating Two Strings -- Searching for a Substring in a String -- Recipe 1-8. Scraping Text from the Web -- Problem -- Solution -- How It Works -- Step 8-1. Install all the necessary libraries
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|a Problem -- Solution -- How It Works -- Step 3-1. Install and import all the necessary libraries -- Step 3-2. Extract text from a Word file -- Recipe 1-4. Collecting Data from JSON -- Problem -- Solution -- How It Works -- Step 4-1. Install and import all the necessary libraries -- Step 4-2. Extract text from a JSON file -- Recipe 1-5. Collecting Data from HTML -- Problem -- Solution -- How It Works -- Step 5-1. Install and import all the necessary libraries -- Step 5-2. Fetch the HTML file -- Step 5-3. Parse the HTML file -- Step 5-4. Extract a tag value
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| 505 |
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|a Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Extracting the Data -- Introduction -- Client Data -- Free Sources -- Web Scraping -- Recipe 1-1. Collecting Data -- Problem -- Solution -- How It Works -- Step 1-1. Log in to the Twitter developer portal -- Step 1-2. Execute query in Python -- Recipe 1-2. Collecting Data from PDFs -- Problem -- Solution -- How It Works -- Step 2-1. Install and import all the necessary libraries -- Step 2-2. Extract text from a PDF file -- Recipe 1-3. Collecting Data from Word Files
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Natural Language Processing
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Traitement automatique des langues naturelles
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| 653 |
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|a Natural language processing (Computer science) / fast
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Machine Learning
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| 653 |
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|a Natural language processing (Computer science) / http://id.loc.gov/authorities/subjects/sh88002425
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| 700 |
1 |
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|a Shivananda, Adarsha
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Step 4-2. Create a custom function for text standardization. - Includes index
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| 024 |
8 |
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|a 10.1007/978-1-4842-7351-7
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| 776 |
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|z 9781484273500
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| 776 |
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|z 1484273508
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| 776 |
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|z 1484273516
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| 776 |
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|z 9781484273517
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781484273517/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3/5
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| 082 |
0 |
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|a 500
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| 520 |
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|a You will: Know the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and more Implement text pre-processing and feature engineering in NLP, including advanced methods of feature engineering Understand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learning
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| 520 |
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|a This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks. After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.
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| 520 |
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|a Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP.
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