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  • Book Overview & Buying Natural Language Processing Fundamentals [Instructor Edition]
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Natural Language Processing Fundamentals [Instructor Edition]

Natural Language Processing Fundamentals [Instructor Edition]

By : Dwight Gunning, Sohom Ghosh
4.4 (41)
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Natural Language Processing Fundamentals [Instructor Edition]

Natural Language Processing Fundamentals [Instructor Edition]

4.4 (41)
By: Dwight Gunning, Sohom Ghosh

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this course, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The course will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)
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Dealing with Semi-Structured Data

We learned about various types of data in Chapter 2, Feature Extraction from Texts. Let's quickly recapitulate what semi-structured data refers to. A dataset is said to be semi-structured if it is not in a row-column format but can be converted into a structured format that has a definite number of rows and columns. Often, we come across data that is stored as key-value pairs or embedded between tags, as is the case with JSON and XML files. These are instances of semi-structured data. The popular semi-structured data formats are JSON and XML.

JSON

JavaScript Object Notation, or JSON, files are used for storing and exchanging data. It is human-readable and easy to interpret. Just like text files and CSV files, JSON files are language independent. This means that different programming languages such as Python, Java, and so on can work with JSON files effectively. In Python, a built-in data structure called dictionary is capable of storing...

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Natural Language Processing Fundamentals [Instructor Edition]
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