Skip to main content

2022 | Book

Natural Language Processing Projects

Build Next-Generation NLP Applications Using AI Techniques

Authors: Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni

Publisher: Apress


About this book

Leverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects. Projects throughout this book grow in complexity and showcase methodologies, optimizing tips, and tricks to solve various business problems. You will use modern Python libraries and algorithms to build end-to-end NLP projects.

The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space.

By the end of this book, you will have the knowledge needed to solve various business problems using NLP techniques.

What You Will Learn

Implement full-fledged intelligent NLP applications with PythonTranslate real-world business problem on text data with NLP techniquesLeverage machine learning and deep learning techniques to perform smart language processingGain hands-on experience implementing end-to-end search engine information retrieval, text summarization, chatbots, text generation, document clustering and product classification, and more

Who This Book Is For

Data scientists, machine learning engineers, and deep learning professionals looking to build natural language applications using Python

Table of Contents

Chapter 1. Natural Language Processing and Artificial Intelligence Overview
In recent years, we have heard a lot about artificial intelligence, machine learning, deep learning, and natural language processing. What are they? Are they all the same? How do we differentiate between them?
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 2. Product360: Sentiment and Emotion Detector
This chapter explores sentiment analysis and emotion detection along with text preprocessing and feature engineering methods. Also, we are exploring different machine learning techniques to train classifier models and evaluate using a confusion matrix. In the second half of the chapter, let’s pull real-time data from Twitter, and predict sentiment and emotions to generate insights. Also, we generate a script for automated reporting, which sends reports to a given set of e-mail addresses. This chapter gives you an overall picture of implementing an end-to-end pipeline that provides powerful insights about any product available on the market.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 3. TED Talks Segmentation and Topics Extraction Using Machine Learning
TED Talks are knowledge videos of talks by experts in technology, entertainment, and design (hence TED). These conferences are arranged across the world. Great speakers come forward and share their experiences and knowledge. These talks are limited to a maximum length of 18 minutes and cover a wide range of topics. The videos are stored, and every video has a description of the video content.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 4. Enhancing E-commerce Using an Advanced Search Engine and Recommendation System
A few decades ago, no one would have ever imagined that we could buy a 55-inch TV at midnight while sitting at home watching a 22-inch TV. Thanks to the Internet and e-commerce, we can buy any item at any time from anywhere, and it is delivered quickly. Flexibility has made e-commerce businesses expand exponentially. You don’t have to visit the store, products have unlimited options, prices are lower, no standing in line to pay, and so forth.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 5. Creating a Résumé Parsing, Screening and Shortlisting System
The objective of this project is to create a résumé shortlisting system using natural language processing.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 6. Creating an E-commerce Product Categorization Model Using Deep Learning
This chapter explores multiclass classification using deep learning. You look at different deep neural networks, like CNN, RNN, and LSTM, and ways to tune and evaluate them.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 7. Predicting Duplicate Questions in Quora
Quora is a question-and-answer platform for folks to connect and gain knowledge on various topics. Anyone can ask a question, and anyone can write answers to these questions, thereby bringing variety in views.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 8. Named-Entity Recognition Using CRF and BERT
Named-entity recognition (NER) is a natural language processing technique. It is also called entity identification or entity extraction. It identifies named entities in text and classifies them into predefined categories. For example, extracted entities can be the names of organizations, locations, times, quantities, people, monetary values, and more present in text.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 9. Building a Chatbot Using Transfer Learning
In today’s world, most businesses need to have customer support for their products and services. With the increase in e-commerce, telecommunication services, Internet-related products, and so on, the demand for customer service is only increasing. The nature of customer service support queries is repetitive in most conversations. Customer support conversations can be automated.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 10. News Headline Summarization
Text summarization is all about converting a bunch of paragraphs into a few sentences that explain the whole document’s gist. There are hundreds of applications in every industry, given the amount of text data. Text data is increasing exponentially. A lot of time is needed to analyze, understand, and summarize each piece of it.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 11. Text Generation: Next Word Prediction
This chapter explores ways to generate text or predict the next word, given the sequence of previous words. Use cases or applications include word/sentence suggestions while typing an e-mail in Gmail or text messages in LinkedIn, and machines writing poems, articles, blogs, chapters of novels, or journal papers.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Chapter 12. Conclusion and Future Trends
You have learned how to build different NLP applications and projects leveraging the power of machine learning and deep learning, which helps solve business problems across industries.
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Natural Language Processing Projects
Akshay Kulkarni
Adarsha Shivananda
Anoosh Kulkarni
Copyright Year
Electronic ISBN
Print ISBN

Premium Partner