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)


This chapter was more than an introduction to the Gensim API. We now know how to load pre-trained GloVe vectors, and you can use these vector representations instead of TD-IDF in any machine learning model.

We looked at why fastText vectors are often better than word2vec vectors on a small training corpus, and learned that you can use them with any ML models.

We learned how to build doc2vec models. You can now extend this doc2vec approach to build sent2vec or paragraph2vec style models as well. Ideally, paragraph2vec will change, simply because each document will be a paragraph instead.

In addition, we now know how we can quickly perform sanity checks on our doc2vec vectors without using an annotated test corpora. We did this by checking the rank dispersal metric.