- Mastering Text Mining with R
- Table of Contents
- Mastering Text Mining with R
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Preface
- 1. Statistical Linguistics with R
- Probability theory and basic statistics
- Probability space and event
- Theorem of compound probabilities
- Conditional probability
- Bayes' formula for conditional probability
- Independent events
- Random variables
- Discrete random variables
- Probability frequency function
- Probability distributions using R
- Cumulative distribution function
- Joint distribution
- Binomial distribution
- Poisson distribution
- Counting occurrences
- Zipf's law
- Heaps' law
- Lexical richness
- Language models
- Quantitative methods in linguistics
- R packages for text mining
- Summary
- Probability theory and basic statistics
- 2. Processing Text
- 3. Categorizing and Tagging Text
- 4. Dimensionality Reduction
- 5. Text Summarization and Clustering
- 6. Text Classification
- Text classification
- Document representation
- Kernel methods
- Bias–variance trade-off and learning curve
- Learning curve
- Dealing with reducible error components
- Summary
- 7. Entity Recognition
- Index
In this chapter, we learnt about entity extraction and recognition using R implementation of OpenNLP. Also, we learnt to use other functionalities present in Apache OpenNLP but not yet implemented in the OpenNLP package in R.
In the next chapters, we will learn about some real-life applications of the techniques we have learnt so far, on social media data.
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