Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Segmentation, annotation, and chunking


When the text is presented in digital form, it is relatively easy to find words as we can split the stream on non-word characters. This becomes more complex in spoken language analysis. In this case, segmenters try to optimize a metric, for example, to minimize the number of distinct words in the lexicon and the length or complexity of the phrase (Natural Language Processing with Python by Steven Bird et al, O'Reilly Media Inc, 2009).

Annotation usually refers to parts-of-speech tagging. In English, these are nouns, pronouns, verbs, adjectives, adverbs, articles, prepositions, conjunctions, and interjections. For example, in the phrase we saw the yellow dog, we is a pronoun, saw is a verb, the is an article, yellow is an adjective, and dog is a noun.

In some languages, the chunking and annotation depends on context. For example, in Chinese, 爱江山人 literally translates to love country person and can mean either country-loving person or love country-person...