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2021 | Buch

Types of Data Kapitel lesen Erstes Kapitel lesen

Practical Natural Language Processing with Python

With Case Studies from Industries Using Text Data at Scale

verfasst von: Mathangi Sri

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python.

Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling.

By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book.

What You Will Learn

Build an understanding of NLP problems in industryGain the know-how to solve a typical NLP problem using language-based models and machine learningDiscover the best methods to solve a business problem using NLP - the tried and tested onesUnderstand the business problems that are tough to solve

Who This Book Is For

Analytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve the problems at hand.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Types of Data
Abstract
Natural language processing (NLP) is a field that helps humans communicate with computers naturally. It is a shift from the era when humans had to “learn” to use computers to computers being trained to understand humans. It is a branch of artificial intelligence (AI) that deals with language. The field dates back to the 1950s when a lot of research was undertaken in the machine translation area. Alan Turing predicted that by the early 2000s computers would be able to flawlessly understand and respond in natural language that you won't be able to distinguish between humans and computers. We are far from that benchmark in the field of NLP. However, some argue that this may not even be the right lens to measure achievements in the field. Be that as it may, NLP is central to the success of many businesses. It is very difficult to imagine life without Google search, Alexa, YouTube recommendations, and so on. NLP has become ubiquitous today.
Mathangi Sri
Chapter 2. NLP in Customer Service
Abstract
Customer service is a multi-billion dollar industry. The cost of bad customer experience is estimated to be a trillion dollar globally (https://blog.hubspot.com/service/customer-service-stats). Customer service has its early origins in the form of call centers in the 1960s. The need for customer service grew, and today customer support has become a sizeable portion of any consumer organization. Customers can contact organizations in a multi-modal way, through the Web, apps, voice, IVR, or calls. In this chapter, we will look at the core problems that NLP solves in human-assisted customer service via chats and calls.
Mathangi Sri
Chapter 3. NLP in Online Reviews
Abstract
Reviews are a significant part of online buying cycles today. Though the “vocal minority” is few, the number of users who are impacted by reviews is significantly large. One study found that 63% of users prefer online sites that have reviews. Customers who visit review pages have an astounding 105% more chance of buying from the website (https://cxl.com/blog/user-generated-reviews/#:~:text=Reevoo%20found%20that%2050%20or,site%20that%20has%20user%20reviews). Mining these reviews gives insights to both the online service provider as well as the seller who has listed the product. Other than knowing whether the customer is happy or not, we can also know how users feel about each feature in the product. Sometimes reviewers write a lot about their lifestyle and the use case they have found for the product as well. This can provide insights into things like product-market fit or the value proposition for the product. This can later be used in brand communications for the product. We can also find opportunities or gaps in a category and hence get the “voice of customer” to create a new product or even start a new business.
Mathangi Sri
Chapter 4. NLP in Banking, Financial Services, and Insurance (BFSI)
Abstract
The banking and financial industries have been making data-driven decisions for more than a century. Since a wrong decision could have heavy cost for a financial institution, they have been one of the early adopters of big data. A lot of machine learning use cases in the banking, financial services, and insurance industries (BFSI) have been using structured data like transactional history or CRM history. However, over the last few years there has been an increasing tendency to use text data to mainstream underwriting and risk or fraud detecting.
Mathangi Sri
Chapter 5. NLP in Virtual Assistants
Abstract
Virtual assistants are intelligent systems that respond to user queries. They can also hold conversations with users. We can trace the origin of virtual assistants to the 1950s when the Turing test was used to distinguish human conversations from machine conversations. NLP has undergone remarkable changes since then, especially in the last decades or so. Natural language generation techniques in machine synthesis responses are very new developments in the field. The chapters so far in this book have been dealing with NLU, or natural language understanding. You saw various ways of extracting intents, sentiments, etc. In NLG, machine learning algorithms are used to generate sentences. You will explore an example of natural language generation for a bot in this chapter.
Mathangi Sri
Backmatter
Metadaten
Titel
Practical Natural Language Processing with Python
verfasst von
Mathangi Sri
Copyright-Jahr
2021
Verlag
Apress
Electronic ISBN
978-1-4842-6246-7
Print ISBN
978-1-4842-6245-0
DOI
https://doi.org/10.1007/978-1-4842-6246-7

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