What is Natural language Processing? How can you put it to use?

Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. Natural Language Processing (NLP) is a fundamental element of  conversational artificial intelligence for communicating with intelligent systems using natural language.

NLP helps computers read and respond by simulating the human ability to understand the everyday language that people use to communicate. In the context of Chatbot, integrating NLP means adding a more human touch. The input and output of an N.L.P system can be −
· Speech based
· Written Text based

Conversational AI which takes in speech as input is known as a Voicebot and the one which takes in text inputs from user is know as Chat bot.

The effectiveness of using NLP is highly dependent on how your chatbot is built, and what you want it to accomplish. When the text has been provided, the computer will utilise algorithms to extract meaning associated with every sentence and collect the essential data from them.

How does natural language processing work?

Syntax and semantic analysis are two main techniques used with natural language Processing. Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules.
Semantics involves the use and meaning behind words. NLP applies algorithms to understand the meaning and structure of sentences.

Most NLP systems parses the natural language utterances in these phases :

Parts of Speech (POS) Tagging

The words in the utterance are tagged to whether its a noun, verb, adjective etc, bringing out the a basic composition of the utterance.

Named Entity recognition (NER)

The named entities like Person-names or location-names are tagged. Not just they are resolved as named entities, they are also tagged to which is the most probable type they are – like PERSON, LOCATION, ORGANISATION etc.

Shallow Chunking

Here the groups of words which usually forms an entity are tagged, and their POS types correctly identified.

Dependency Parsing

The syntactic and semantic dependencies between the identified entities and verbs are tagged. For eg, subject, direct object etc.

Intent Recognition

Intents are simple, discrete tasks like “Book a Table”, “Transfer Funds”, “Booking Cancellation”, and are typically described with a verb and noun combination. The goal of Intent recognition is to correctly match a user’s spoken or typed in utterance with the intended task or question with the help of machine learning and/or meaning match algorithms.

Benefits of Choosing NLP based Chatbots or Voicebots

Better results all the way around – NLP delivers applicable results as soon as your customer search or enquire anything.

Enhance customer satisfaction through smart, automated customer service that provides easily-understandable information.

Natural language processing can be leveraged by companies to improve the efficiency of documentation processes, improve the accuracy of documentation, and identify the most pertinent information from large databases.

Reducing Administrative Work – Form-filling, data entry, and other mundane administrative tasks require time, manpower, and money.

Sachin Kaushik

Author Sachin Kaushik

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