Key Differences between Text Mining vs Natural Language ProcessingNatural language is what we use for communication.  Techniques for processing such data to understand underlying meaning is collectively called as Natural Language Processing (NLP). The data could be speech, text or even an image and approach involve applying Machine Learning (ML) techniques on data to build applications involving classification, extracting structure, summarizing and translating data.

Text Mining vs Natural Language Processing Comparison Table

Basis Of Comparison Text mining NLP
Goal Extract high-quality information
from unstructured and structured text.Information could be patterned
in text or matching structure but the semantics in the text is not considered.
Trying to understand what is conveyed in natural language by human- may text or speech. Semantic and grammatical structures are analyzed.
Tools
  • Text processing languages
  • Statistical models
  • ML models
  • Advanced ML models
  • Deep Neural Networks
  • Toolkits like NLTK in Python
Scope
  • Data sources are documented collections
  • Extracting representative features for natural language documents
  • Input for a corpus-based computational linguistic
  • Data source can be any form of natural human communication method like text, speech, signboard etc
  • Extracting semantic meaning and grammatical structure from the input
  • Making all level of interaction with machines more natural for human

 

Outcome Explanation of text using statistical indicators like

  • Frequency of words
  • Patterns of words
  • Correlation within words
Understanding what conveyed through text or speech like

  • Conveyed sentiment
  • The semantic meaning of the text so that it can be translated to other languages
  • Grammatical structure
System Accuracy Performance measure is direct and relatively simple.Here we have clearly measurable mathematical concepts. Measures can be automated Highly difficult to measure system accuracy for machines.Human intervention is needed most of the time.For example, consider an NLP system, which translates from English to Hindi.Automate the measure of how accurately

 

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