Natural Language Processing (NLP) is “ability of machines to understand and interpret human language the way it is written or spoken”.
The objective of NLP is to make computer/machines as intelligent as human beings in understanding language.
The ultimate goal of NLP is to the fill the gap how the humans communicate(natural language) and what the computer understands(machine language).
There are three different levels of linguistic analysis done before performing NLP –
Syntax – What part of given text is grammatically true.
Semantics – What is the meaning of given text?
Pragmatics – What is the purpose of the text?
NLP deal with different aspects of language such as
- Phonology – It is systematic organization of sounds in language.
- Morphology – It is a study of words formation and their relationship with each other.
Approaches of NLP for understanding semantic analysis
- Distributional – It employs large-scale statistical tactics of Machine Learning and Deep Learning.
- Frame – Based – The sentences which are syntactically different but semantically same are represented inside data structure (frame) for the stereotyped situation.
- Theoretical – This approach is based on the idea that sentences refer to the real word (the sky is blue) and parts of the sentence can be combined to represent whole meaning.
- Interactive Learning – It involves pragmatic approach and user is responsible for teaching the computer to learn the language step by step in an interactive learning environment.
The true success of NLP lies in the fact that humans deceive into believing that they are talking to humans instead of computers.
Why Do We Need NLP?
With NLP, it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time.
Due to the presence of large data (text) around, why not we use the computers untiring willingness and ability to run several algorithms to perform tasks in no time.
These tasks include other NLP applications like Automatic Summarization (to generate summary of given text) and Machine Translation (translation of one language into another)
Process of NLP
In case the text is composed of speech, speech-to-text conversion is performed.
The mechanism of Natural Language Processing involves two processes:
- Natural Language Understanding
- Natural Language Generation
Natural Language Understanding
NLU or Natural Language Understanding tries to understand the meaning of given text. The nature and structure of each word inside text must be understood for NLU. For understanding structure, NLU tries to resolve following ambiguity present in natural language:
- Lexical Ambiguity – Words have multiple meanings
- Syntactic Ambiguity – Sentence having multiple parse trees.
- Semantic Ambiguity – Sentence having multiple meanings
- Anaphoric Ambiguity – Phrase or word which is previously mentioned but has a different meaning.
Next, the meaning of each word is understood by using lexicons (vocabulary) and set of grammatical rules.
However, there are certain different words having similar meaning (synonyms) and words having more than one meaning (polysemy).
Natural Language Generation