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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
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Alexey Grigorev
Richard M. Reese Richard M. Reese
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Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
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Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Extracting relationships from sentences


Knowing the relationship between elements of a sentence is important in many analysis tasks. It is useful for assessing the important content of a sentence and providing insight into the meaning of a sentence. This type of analysis has been used for tasks ranging from grammar checking to speech recognition to language translations.

In the previous section, we demonstrated one approach used to extract the parts of speech. Using this technique, we were able to identify the sentence element types present in a sentence. However, the relationships between these elements is missing. We need to parse the sentence to extract these relationships between sentence elements.

 

Using OpenNLP to extract relationships

There are several techniques and APIs that can be used to extract this type of information. In this section we will use OpenNLP to demonstrate one way of extracting the structure of a sentence. The demonstration is centered around the ParserTool class...

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