This post is a continuation of the below post which discussed Words, Sentences, Morphology, and Pragmatics.

NLP Learnings 03 – What is Linguistic in NLP and Linguistic Categories or Linguistic Levels

Fundamentally a “Sentence” is a group of words with meaning. In the context of this blog post, what an NLP engineer can do with a sentence?

First, let us understand “What is a Good Sentence”?

A Good Sentence is one 

• from context perspetive
  • is grammatically correct
  • easy to read
  • natural sounding
• from stylistics perspective
  • does it have both casual words and formal words 

NLP Tasks with a Sentence

In NLP Tasks with a sentence, one can perform:

1. Give a score to the sentence – a number based on the quality of the sentence. Quality can be defined say for example, in a Medical Claims Processing software application, by how many words in the sentence are related to the medical claim business, and based on this we can give a score. Another scoring mechanism could be based on semantics (meaning of a sentence), we can score the sentence as if is it a “request”, “command” or “question” and more.
2. Rank Two Sentences – if we have two sentences then in NLP we can Rank those two sentences, again based on the quality as mentioned in above point 1.
3. Grammatical Equality Between Sentences – We can choose a better sentence from a list of sentences. Better he could be Grammatical Equality or Good Sentence vs Bad Similar Sentence.
4. Predict Next Sentence – While typing for a search or writing a document we need a predictive next sentence to make our search typing or writing faster and better word usage.
5. Translate sentence to another language – we can translate from say English to Telugu or Hindi or Japanese or German.
6. Frequency of words – just split a sentence and get the frequency of each word for any statistical analysis later

 

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