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

Training the model

We will train a model for matching these question pairs. Let's start by importing the relevant libraries, as follows:

import sys
import os
import pandas as pd
import numpy as np
import string
import tensorflow as tf

Following is a function that takes a pandas series of text as input. Then, the series is converted to a list. Each item in the list is converted into a string, made lower case, and stripped of surrounding empty spaces. The entire list is converted into a NumPy array, to be passed back:

def read_x(x):
x = np.array([list(str(line).lower().strip()) for line in x.tolist()])
return x

Next up is a function that takes a pandas series as input, converts it to a list, and returns it as a NumPy array:

def read_y(y):
return np.asarray(y.tolist())

The next function splits the data for training and validation. Validation data is helpful to see how well...