Implement NLP use-cases using BERT: Explore the Implementation of NLP Tasks Using the Deep Learning Framework and Python (English Edition)

By Amandeep

This book provides a solid foundation for ‘Natural Language Processing’ with pragmatic explanation and implementation of a wide variety of industry wide scenarios. After reading this book, one can simply jump to solve real world problems and join the league of NLP developers.
It starts with the introduction of Natural Language Processing and provides a good explanation of different practical situations which are currently implemented across the globe. Thereafter, it takes a deep dive into the text classification with different types of algorithms to implement the same. Then, it further introduces the second important NLP use case called Named Entity Recognition with its popular algorithm choices. Thereafter, it provides an introduction to a state of the art language model called BERT and its application.
After reading this book, you would be prepared to start picking any NLP applications, have a healthy discussion about the pros and cons of different approaches with other team members, and definitely implement a good NLP model.
Finally, at the end of this book you will connect with all the theoretical discussions with code snippets (Python) which would be really helpful to implement into your domain-specific applications.

• Language: English
• Publisher: BPB Online LLP
• Release date: Apr 17, 2021
• ISBN: 9789390684694

Book preview

CHAPTER 1

Introduction to NLP and Different Use-Cases

Introduction

This chapter will introduce the Natural Language Processing concept in simple words that can easily be understood by any novice. Additionally, readers would get to know a brief introduction of 10+ NLP use-cases, which will build better exposure to the applicability of NLP, along with fundamental pre-processing steps.

Structure

Understanding of NLP

List of NLP use-cases

Brief Introduction of each use-case

Text pre-processing techniques

Conclusion

Objective

To enable the reader for contributing to any discussion related to NLP Use-Cases by providing his views or understanding around the topic.

1.1 What is Natural Language Processing or NLP?

NLP is an acronym for multiple terms, but in this book, it will stand for Natural Language Processing. Natural language processing (NLP) is a subfield overlapping artificial intelligence, computer science, and linguistics that focuses on aiding computers to understand the natural language of humans.

It empowers machines to understand the text and the enormous amount of unstructured data, which is growing exponentially. Hence, we can say that NLP will play a pivotal role in next-generation computer systems.

The ultimate objective of NLP is to classify, extract, understand, translate, correct, paraphrase documents written in human languages and thus, utilizes the same to search for answers automatically for the questions or queries asked.

It has been published on various websites that Machine Learning Engineer and Data Scientists come under the top 10 most demanding jobs in the software industry that has the responsibility to inject intelligence in the applications. We have reached some level of saturation for learning from organized or structured data, and there is an ample amount of focus required to extract insights from unstructured data (approximately 80% of internet data is unstructured). It seems to me that text data should be at least 50% of unstructured data. Therefore, finding insights from the text is the need of the hour.

1.2 NLP use-cases

There are a plethora of NLP use-cases (a few of them are overlapping as well), and brief introductions of the same are provided below. We will take a deep dive into the first two of these in subsequent chapters.

Text/Document/Sentence classification

Emotion classification or sentiment analysis

Subjectivity analysis

Sarcasm detection:

Intent classification

Hate speech detection

Information extraction

Named entity recognition

QA (questions and answers)

Chat-bot

Relation extraction

Entity linking

Text summarization

Morphological analysis

Semantic textual similarity

Word sense disambiguation

Spelling correction

Grammatical error correction

Language Modeling

Slot filling

Topic Modeling

Paraphrase generation

1.3 Quick sneak on NLP use-cases

Let's take a quick overview of mentioned use-cases:

Text/Document/Sentence classification

It is a supervised machine learning problem where we classify text into one of the predefined categories such as whether the text is having polite language or offensive one.

Text or document may be interchanged, though it depends upon the individual. Additionally, these can have single or multiple sentences. So, some may use document classification for even sentence classification.

There are multiple applications of text classification, which are described below:

Emotion classification or sentiment analysis

Although some believe that sentiments [shown in square shapes] are after-effects of emotions [shown in oval shapes], we have considered it as one application.

Figure 1.1: Sentiments and Emotions

In this use-case, the task is to classify emails into any of 3 sentiments/emotions: positive, negative, or neutral.

Real-life