NLP Learnings 05 – Sentence Linguistic Analysis – Pragmatics Analysis

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This post is the continuation of the previous post i.e. NLP Learnings 04

What is Pragmatics Analysis?

Suppose say you take a sentence “Can you help me?”

From the semantics i.e. meaning perspective, it can be analyzed as a question asked by someone to another person.

But from the context of two people talking, it is “a request” made by a person to another person. Here the “request” is more important and it is Pragmatics Analysis.

So Pragmatics Analysis includes analyzing a sentence in the context of a social aspect i.e. social means 2 or more people included in the context where the sentence was said or written.

Where Prgamtics Analysis is used?

It is useful in language-to-language machine translation, dialogue analysis 

 

Related Topics:

  1. Natural Language Processing Primer
  2. NLP Learnings 02 – After Primer – NLP Models & Evaluating NLP Models
  3. NLP Learnings 03 – What is Linguistic in NLP and Linguistic Categories or Linguistic Levels
  4. NLP Learnings 04 – Sentence Linguistic Analysis – Words As First Step
  5. NLP Learnings 05 – Sentence Linguistic Analysis – Pragmatics Analysis
  6. NLP Learnings 06 – What can we do with A Sentence in NLP Tasks
  7. NLP Learnings 07 – Introducing NLP Language Models
  8. NLP Learnings 08 – Language Models – Probability Types
  9. NLP Learnings 09 – Language Models – Measuring Text Based On Probability
  10. NLP Learnings 10 – Language Models – Defining Word Boundaries
  11. NLP Learnings 11 – Language Models – Your Business Text Data and Their Words Representation
  12. NLP Learnings 12 – Language Models – Regularization Techniques And Your Business Text Data
  13. NLP Learnings 13 – Language Models – Smoothing Regularization Techniques And Your Business Text Data
  14. NLP Learnings 14 – Language Models – NLP Key Terms and Concepts
  15. NLP Learnings 15 – Language Models – Classifiers Introduction
  16. NLP Learnings 16 – Language Models – Classifiers – What are Probabilistic Classifiers
  17. NLP Learnings 17 – Language Models – Classifiers – Simple Classifiers Introduction
  18. NLP Learnings 18 – Language Models – Classifiers – Simple Classifiers – Linear Probabilistic Classifier Introduction
  19. NLP Learnings 19 – Language Models – Classifiers – Evaluating Classifiers – Precision Recall F-Score Confusion Matrix
  20. NLP Learnings 20 – Language Models – Classifiers – Represent Words In A Document – Choosing and Representing Features In The Right Way
  21. NLP Learnings 21 – Language Models – Word Embeddings
  22. NLP Learnings 22 – Language Models – Sentence and Document Embeddings
  23. NLP Learnings 23 – Language Models – Sentence and Document Embeddings – Which one to consider
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