NLP Learnings 04 – Sentence Linguistic Analysis – Words As First Step

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Sentence Linguistic Analysis – First Step is “Words”

In the previous post i.e. “NLP Learnings 03” we learned different categories or levels of Linguistic Analysis of a Sentence. This post is a continuation of the “NLP Learnings 03” post. 

For a given sentence say for example “Data Scientists use various Natural Language Processing techniques” if we need to do Sentence Linguistic Analysis, first we need to analyze the basic building block of a sentence i.e. Word.

Words Analysis – Phonetics

Analyze the Sounds of Words i.e. movements of tongue and lips to utter a word

This Phonetics Word analysis is important for “Speech to Text” or “Text – to – Speech” NLP tasks.

Words Analysis – Phonology

Analyze the Sounds of Words from a Grammar perspective i.e. in a given language say English or Hindi or Telugu, how each word contrast with one another like how they are distributed or patterned.

This Phonology Word analysis is important for “Speech to Text” or “Text – to – Speech” or Information Extract kind of NLP tasks.

Words Analysis – Syntax

Analyzing how words, in a sentence, combine together to form Phrases is called “Syntax Analysis” of a sentence.

Useful in IR (Information Extraction), Question Answering, Natural Language Inference, 

Words Analysis – Semantics

Analyzing a sentence from its meaning perspective is called Semantics

Useful in Question Answering, Natural Language Inference, Text Summarization

 

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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