Book Image

Natural Language Processing with Python Quick Start Guide

By : Nirant Kasliwal
Book Image

Natural Language Processing with Python Quick Start Guide

By: Nirant Kasliwal

Overview of this book

NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.
Table of Contents (10 chapters)

Quick code means word vectors and heuristics

For the sake of simplicity, we will assume that our bot does not need to remember the context of any question. Therefore it sees input, responds to it, and is done. No links are established with the previous input.

Let's start by simply loading the word vectors using gensim:

import numpy as np
import gensim
print(f"Gensim version: {gensim.__version__}")

from tqdm import tqdm
class TqdmUpTo(tqdm):
def update_to(self, b=1, bsize=1, tsize=None):
if tsize is not None: = tsize
self.update(b * bsize - self.n)

def get_data(url, filename):
Download data if the filename does not exist already
Uses Tqdm to show download progress
import os
from urllib.request import urlretrieve

if not os.path.exists(filename):

dirname = os.path.dirname(filename...