Mathangi Sri - Practical Natural Language Processing with Python: With Case Studies from Industries Using Text Data at Scale

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I am fortunate to have had the exposure and opportunity to solve complex NLP problems that benefited various businesses across geographies. This book comes from my learnings and hence it is a practitioner view of solving text problems. Solving NLP problems involves a certain combination of creativity and technical knowledge. Sometimes the best deep learning methods do not solve a problem as well as simple solutions do. I am always reminded of Occams razor, which states that when there are alternatives available, the simplest one possibly solves the problem best.

In my opinion, the answer to any problem lies in the data. This is why the first chapter talks about text data. I cover different types of text data and the information that can be extracted from this data. Chapters is dedicated to virtual assistants . I explore techniques to build bots using state-of-the-art neural network architectures. This chapter also introduces natural language generation concepts.

I want to thank my husband, Satish. I brainstormed with him several times during the course of this book on the technical and business use cases. I also want to thank the team at Apress for providing adequate reviews and guidance on the content. I learned all the concepts on the job, so I thank all the people with whom I have had the privilege to work with and learn from.

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 wont 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.