Natural Language Processing
When natural meets artificial, machine comes to life as if it is a real human in action.
Computers are great at working with structured data like spreadsheets and database tables. But humans usually communicate in words, not in tables. That’s unfortunate for computers. A lot of information in the world is unstructured — raw text in English or another human language. How can we get a computer to understand unstructured text and extract data from it?
Natural Language Processing is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
natural language processing can help machines automatically understand text.
Voice-based systems like Alexa or Google Assistant need to translate your words into text.
•A sub field of Artificial intelligence(AI).
- Aim: To build intellingent computer that can interact with human being like a human being.
- A sub field of Artificial intelligence(AI).
NLP Components Types :
NLP is classified basically into two major components
Natural Language Understanding (NLU):
- Mapping the given input in natural language into useful representations.
- Analyzing different aspects of the language.
Natural Language Generation (NLG):
It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation
Classical symbolic methods:
Morphological analysis:
Individual words are analysed into their component and non word token
punctuation are separated from word
Syntactic analysis:
grammatical structure of sentence is analysed
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Semantic analysis:
determine possible meaning of sentence
Discourse Analysis:
process of determining information that is useful for performing other tasks
Pragmatic analysis:
It derives knowledge from external common sense information
Major Areas for Research & Development
- Text Processing
- Automatic Speech Recognition(ASR)
- Speech Processing
- Text to Speech(TTS)
- Speech to Speech Translation
Industrial Application:
- Voice assistance(Alexa)
- Search Engines
- Advanced Text Editors
- Information Extraction
- Sentiment analysis
- Computational Advertising
Conclusion:
NLP is assuming an important role in Data Science. Industry experts have predicted that the demand for NLP experts will grow exponentially in the near future.
NLP makes easier to handle a large volumes of data.
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