Free eBook: Hands-On Natural Language Processing with Python

Hands-On Natural Language Processing with Python
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow

Rajesh Arumugam and Rajalingappaa Shanmugamani, 312 pages, Jul 2018

Key Features

  • Weave neural networks into linguistic applications across various platforms
  • Perform NLP tasks and train its models using NLTK and TensorFlow
  • Boost your NLP models with strong deep learning architectures such as CNNs and RNNs


This book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. The book equips you with practical knowledge to implement deep learning in your linguistic applications using NLTk and Python's popular deep learning library, TensorFlow.

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Chapter 1


Getting Started

Natural language processing (NLP) is the field of understanding human language using computers. It involves the analysis and of large volumes of natural language data using computers to glean meaning and value for consumption in real-world applications.

Chapter 2


Text Classification and POS Tagging Using NLTK

The Natural Language Toolkit (NLTK) is a Python library for handling natural language processing (NLP) tasks, ranging from segmenting words or sentences to performing advanced tasks, such as parsing grammar and classifying text. NLTK provides several modules and interfaces to work on natural lang...

Chapter 3


Deep Learning and TensorFlow

Applications that leverage natural language processing (NLP) have begun to achieve close to human-level accuracy in tasks such as language translation, text summarization, and text-to-speech, due to the adoption of deep learning models. This widespread adoption has been driven by two key developm...

Chapter 4


Semantic Embedding Using Shallow Models

In this chapter, we will discuss the motivation for understanding semantic relationships between words, and we will discuss approaches for identifying such relationships. In the process, we will obtain a vector representation for words, which will let us build vector representations at a document...

Chapter 5


Text Classification Using LSTM

Text classification is the task of tagging natural language texts or unstructured text to one of the categories from a predefined set. Identifying positive-negative sentiments in product reviews, categorizing news articles, and segmenting customers based on their conversations about products in s...

Chapter 6


Searching and DeDuplicating Using CNNs

Deep neural networks have been proven to work extremely well when provided with a large number of data points. However, a dearth of data for building large search engines has been a primary issue for most engines. Traditional approaches to searching in text data involved domain understanding and...

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