Learning Path: Java: Natural Language Processing with Java
Natural Language Processing is used in many applications to provide capabilities that were previously not possible. It involves analyzing text to obtain the intent and meaning, which can then be used to support an application. Using NLP within an application requires a combination of standard Java techniques and often specialized libraries frequently based on models that have been trained. If you're interested to learn the powerful Natural Language Processing techniques with Java, then go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are:
- Perform tokenization based on specific text processing needs
- Extract the relationship between elements of text
This Learning Path covers the essence of NLP using Java. This Learning Path will commence by walking you through basic NLP tasks including data acquisition, data cleaning, finding parts of text, and determining the end of sentences. These serve as the basis for other NLP tasks such as classifying text and determining the relationship between text elements. This will be followed by the use of tokenization techniques. Tokenization is used for almost all NLP tasks. You’ll learn how text can be split to reveal information such as names, dates, and even the grammatical structure of a sentence. These types of activity can lead to insights into the relationships between text elements and embedded meaning in a document.
You'll then start by building on the basic NLP tasks of data normalization, tokenization, and SBD to perform more specialized NLP tasks. You’ll be able to do more than simply find a word in the text. You'll also identify specific elements such as a person’s name or a location from the text. Finally, you'll learn to split a sentence into basic grammatical units is another task that enables you to extract meaning and relationships from text.
Towards the end of this Learning Path, you will be ready to take on more advanced NLP tasks with Natural Language Processing techniques using Java.
Meet Your Experts:
We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:
- Kamesh Balasubramanian is the founder and CEO of Wirecog, LLC. He is the inventor of Wireframe Cognition (Wirecog), an award-winning, patented technology that allows machines to understand wireframe designs and produce source code from them. Kamesh has over 20 years' software development experience and has implemented numerous solutions in the advertising, entertainment, media, publishing, hospitality, videogame, legal, and government sectors. He is an award-winning, professional member of the Association for Computing Machinery and an InfyMaker Award winner. He was recognized as a Maker of Change at the 2016 World Maker Faire in New York and, upon request, has demonstrated Wirecog at MIT.
- Ben Tranter is a developer with nearly six years’ experience. He has worked with a variety of companies to build applications in Go, in the areas of data mining, web back ends, user authentication services, and developer tools, and is a contributor to a variety of open source Go projects.
- Rostislav Dzinko is a software architect who has been working in the software development industry for more than six years. He was one of the first developers who started working with the Go language far earlier than the first official public release of Go 1.0 took place. Rostislav uses the Go language daily and has successfully used it in production for more than two years, building a broad range of software from high-load web applications to command-line utilities. He has a Master’s degree in Systems Engineering and has completed a PhD thesis.
Who is the target audience?
- This Learning Path is aimed at Java developers who wish to learn the basics of NLP. Such developers will be working on applications that can benefit from text analysis, whether from providing more sophisticated processing of user input, or adding analytical capabilities to enhance the user's understanding of an application's data sets.
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