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

Transfer learning using GloVe embeddings

Global Vectors (GloVe) uses global word-word co-occurrences statistics in a large text corpus to arrive at dense vector representations of words. It is an unsupervised learning method with the objective of making the dot product of the learned vectors equal to the logarithm of the probability of co-occurrences. This translates to differences in vectors in the embedding space as the ratio of logarithms of ratio equals to the difference in logarithms.

For this example, we will use the GloVe embedding, which is pre-trained on Twitter data. This data consists of around 2 billion tweets with a vocabulary size of 1.2 million. For the classification task, we use the customer reviews or ratings of Amazon instant videos. First, we must load the reviews data in JSON format and convert it to a pandas DataFrame, as shown in the following code:

json_data...