Applications of NLP (Natural Language Processing)
Natural Language Processing, or NLP, is an artificial intelligence branch that focuses on communicating between computers and humans based on natural language. Few applications of NLP are as follows
Text Prediction
Text prediction refers to the process of estimating the next word in a phrase or sentence.
It is very common NLP application that we use on daily basis, for example how a predictive text appears in keyboard when we’re typing in apps like WhatsApp, Facebook, e-mails, or even Google search or YouTube search.
Using a natural language processing (NLP) method called BERT (Bidirectional Encoder Representations from Transformers), Google creates pre-trained models with neural networks. They are trained using a huge amount of unannotated text on the internet. The BERT algorithm helps the search engine to understand queries similar to a human.
Spell Check
A spell checker is a software application that identifies and corrects spelling mistakes or typos in a text. Grammarly is a widely used example of spell checking software. It automatically detects spelling errors as the user writes and suggests corrections as a result.
Although Spell Checkers have great importance in software applications used for creating and formatting documents, they are also a vital part of websites search where there is a high chance of users making spelling errors or typos, which can affect the search results.
Sentimental Analysis
Now days, Social media is attracting young generation towards it. Social media is computer-based technology that is used to share idea, opinion and thoughts within society. Twitter is example of social media in which it allows to registered users to broadcast the thoughts in short posts called as tweets. Sentiment analysis is nothing but mining of thoughts or opinions or attitude of speaker. It is process of analyzing whether short part of writing is positive, negative and neutral. In this paper, we try to analyze the twitter posts about the movie using lexicon based approach.
Naive Bayes algorithm
Naive Bayes is a classifier which is an algorithm for text classification. This model is easy to build and mostly useful for biggest dataset. This algorithm is mostly used for text classification or for analysing as compared to other models it have higher success rate. As a result it is widely used for Sentiment Analysis (like in social media analysis to classify positive negative views).
The mathematical form to calculate the word models using Naive Bayes. The basic concept is to calculate the probability of word belonging to any possible classes.
Smart Assistants
Smart assistants such as Alexa and Siri recognize patterns in speech using voice recognition, then infer meaning and provide a useful response. We’re used to saying “Ok Google” and getting answers based on context when we ask a question.
Steps :
- Record voice and convert to code
- Identify patterns
- This data provided to the NLP algorithm to identify the meaning of the input
- Based on meaning smart assistant will do the action.
Example :
When we ask google assistant to “Play a Song”. It recognizes our command and plays song in music player.
In conclusion, there are many exciting applications of natural language processing.
Business processes huge volumes of unstructured data, such as customer support tickets, social media posts, surveys, and more, by using natural language processing.
Additionally, they can be used to reduce the time spent on time-consuming tasks by automating them.