Book Image

Hands-On Natural Language Processing with Python

By : Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy
Book Image

Hands-On Natural Language Processing with Python

By: Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy

Overview of this book

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.
Table of Contents (15 chapters)
6
Searching and DeDuplicating Using CNNs
7
Named Entity Recognition Using Character LSTM

Installing NLTK and its modules

Before getting started with the examples, we will set the system up with NLTK and other dependent Python libraries. The pip installer can be used to install NLTK, with an optional installation of numpy, as follows:

sudo pip install -U nltk
sudo pip install -U numpy

The NLTK corpora and various modules can be installed by using the common NLTK downloader in the Python interactive shell or a Jupyter Notebook, shown as follows:

import nltk
nltk.download()

The preceding command will open an NLTK Downloader, as follows. Select the packages or collections that are required:

As shown in the preceding screenshot, specific collections, text corpora, NLTK models, or packages, can be selected and installed. Navigate to stopwords and install it for future use. The following is a list of modules that are required for this chapter's examples:

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