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)

Document embedding

Document embedding is often considered an underrated way of doing things. The key idea in document embedding is to compress an entire document, for example a patent or customer review, into one single vector. This vector in turn can be used for a lot of downstream tasks.

Empirical results show that document vectors outperform bag-of-words models as well as other techniques for text representation.

Among the most useful downstream tasks is the ability to cluster text. Text clustering has several uses, ranging from data exploration to online classification of incoming text in a pipeline.

In particular, we are interested in document modeling using doc2vec on a small dataset. Unlike sequence models such as RNN, where a word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent. This word order independence means...