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210123 ||| eng |
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|a 9781800200807
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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 Chopra, Rohan
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| 245 |
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|a The natural language processing workshop
|b confidently design and build your own NLP projects with this easy-to-understand practical guide
|c Rohan Chopra [and five others]
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| 260 |
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|a Birmingham, UK
|b Packt Publishing
|c 2020
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| 300 |
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|a 1 volume
|b illustrations
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| 505 |
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|a Lemmatization -- Exercise 1.08: Extracting the Base Word Using Lemmatization -- Named Entity Recognition (NER) -- Exercise 1.09: Treating Named Entities -- Word Sense Disambiguation -- Exercise 1.10: Word Sense Disambiguation -- Sentence Boundary Detection -- Exercise 1.11: Sentence Boundary Detection -- Activity 1.01: Preprocessing of Raw Text -- Kick Starting an NLP Project -- Data Collection -- Data Preprocessing -- Feature Extraction -- Model Development -- Model Assessment -- Model Deployment -- Summary -- Chapter 2: Feature Extraction Methods -- Introduction -- Types of Data
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| 505 |
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|a Categorizing Data Based on Structure -- Categorizing Data Based on Content -- Cleaning Text Data -- Tokenization -- Exercise 2.01: Text Cleaning and Tokenization -- Exercise 2.02: Extracting n-grams -- Exercise 2.03: Tokenizing Text with Keras and TextBlob -- Types of Tokenizers -- Exercise 2.04: Tokenizing Text Using Various Tokenizers -- Stemming -- RegexpStemmer -- Exercise 2.05: Converting Words in the Present Continuous Tense into Base Words with RegexpStemmer -- The Porter Stemmer -- Exercise 2.06: Using the Porter Stemmer -- Lemmatization -- Exercise 2.07: Performing Lemmatization
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|a Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Natural Language Processing -- Introduction -- History of NLP -- Text Analytics and NLP -- Exercise 1.01: Basic Text Analytics -- Various Steps in NLP -- Tokenization -- Exercise 1.02: Tokenization of a Simple Sentence -- PoS Tagging -- Exercise 1.03: PoS Tagging -- Stop Word Removal -- Exercise 1.04: Stop Word Removal -- Text Normalization -- Exercise 1.05: Text Normalization -- Spelling Correction -- Exercise 1.06: Spelling Correction of a Word and a Sentence -- Stemming -- Exercise 1.07: Using Stemming
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| 505 |
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|a Exercise 2.08: Singularizing and Pluralizing Words -- Language Translation -- Exercise 2.09: Language Translation -- Stop-Word Removal -- Exercise 2.10: Removing Stop Words from Text -- Activity 2.01: Extracting Top Keywords from the News Article -- Feature Extraction from Texts -- Extracting General Features from Raw Text -- Exercise 2.11: Extracting General Features from Raw Text -- Exercise 2.12: Extracting General Features from Text -- Bag of Words (BoW) -- Exercise 2.13: Creating a Bag of Words -- Zipf's Law -- Exercise 2.14: Zipf's Law -- Term Frequency-Inverse Document Frequency (TFIDF)
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| 505 |
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|a Exercise 2.15: TFIDF Representation -- Finding Text Similarity -- Application of Feature Extraction -- Exercise 2.16: Calculating Text Similarity Using Jaccard and Cosine Similarity -- Word Sense Disambiguation Using the Lesk Algorithm -- Exercise 2.17: Implementing the Lesk Algorithm Using String Similarity and Text Vectorization -- Word Clouds -- Exercise 2.18: Generating Word Clouds -- Other Visualizations -- Exercise 2.19: Other Visualizations Dependency Parse Trees and Named Entities -- Activity 2.02: Text Visualization -- Summary -- Chapter 3: Developing a Text Classifier -- Introduction
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| 505 |
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|a Includes bibliographical references
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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 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 Natural language processing (Computer science) / http://id.loc.gov/authorities/subjects/sh88002425
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| 041 |
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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 "Data science & artificial intelligence."
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| 776 |
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|z 1800208421
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| 776 |
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|z 9781800208421
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781800208421/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 500
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| 082 |
0 |
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|a 006.35
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| 520 |
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|a Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data Key Features Get familiar with key natural language processing (NLP) concepts and terminology Explore the functionalities and features of popular NLP tools Learn how to use Python programming and third-party libraries to perform NLP tasks Book Description Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP.
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| 520 |
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|a You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
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| 520 |
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|a What you will learn Obtain, verify, clean and transform text data into a correct format for use Use methods such as tokenization and stemming for text extraction Develop a classifier to classify comments in Wikipedia articles Collect data from open websites with the help of web scraping Train a model to detect topics in a set of documents using topic modeling Discover techniques to represent text as word and document vectors Who this book is for This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly
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