We'll never know how much data is produced each and every day. Even if, Google, Amazon, and Facebook want to compute the total data on their server, they will fail. Some of the facts about the world's data:
⚡ The experts in the year 2013 believed that 90% of the world's data was generated between the year 2010 to 2012.
⚡ There was a significant rise in the data created every day, around 2.5 quintillion in 2018.
⚡ At the end of 2020, the experts predict that the total number of bytes in the digital universe was 40 times more than the number of stars in the observable universe.
⚡ By 2025, the daily generate data is reached from 2.5 quintillion to 463 Exabyte globally.
⚡ If we talk about only single platform Google, then it handles approximately 1.2 trillion searches every year.
Luckily, we have technologies nowadays, so that we can use this data for a better world. But, it is not a simple task. Currently, there are only 21% of the data are in a structured format. So, it's our need to know technologies like Machine Learning, Deep Learning, Data Mining, Data Analysis, and Data Processing.
In this article, we will see one of the most popular technologies Natural Language Processing(NLP).
What is Text Mining?
The Text Analytics or Text Mining is the process of deriving meaningful information from the natural language text. It involves the process of structuring the input text, deriving patterns with structured data, and interpreted output.
As text mining refers to the process of deriving high-quality information from the text, the overall goal is, essentially to turn text into data for analysis via the application of Natural Language Processing.
What is NLP?
Natural Language refers to the way we humans communicate with each other. Mostly, these communicate are on chat, voice calls, and video calls. Every day we face textural data in the form of signs, emails, web pages, SMS, menus, and so more. In fact, this list is endless. So, to use and understand these data we have technology called NLP.
Natural Language Processing refers to the artificial intelligence method to communicate with the intelligence system using Natural Language.
Applications of NLP
- Sentimental Analysis
- Speech Recognition
- ChatBot
- Machine Translation
- Spell Checking
- Keyword Searching
- Information Extraction
- Advertisement Matching
NLP is divided into two major components:
- Natural Language Understanding (NLU)
- Natural Language Generation (NLG)
Natural Language Understanding involves the following tasks -
- Mapping input to the useful representation
- Analysing different aspects of language
Natural Language Generation involves the following tasks -
- Text Planning
- Sentence Planning
- Text Realization
But, for NLP it's hard to English consideration. Due to this
problem, there are lots of ambiguity.
Types of Ambiguity
Lexical Ambiguity
It is also sometimes referred to as Symmetric ambiguity. This type of NLP ambiguity represents the word, which can have multiple meanings.
For example -
A boy is looking for a match.
Here we not sure about, which match is a boy looking for? Is it a Game match (Cricket, Football, Tennis match) or a partner?
Syntactic Ambiguity
This type of NLP ambiguity represents the sentences with two or more possible meanings. It also called Structure ambiguity or Grammatical ambiguity.
For example -
The chicken is ready to eat.
Is chicken is ready to eat his food or chicken is ready for us to eat?
Referential Ambiguity
This type of ambiguity arises when we referring something using a pronoun.
For example -
The boy told his father the theft. He was very upset.
Here, what 'He' represents? The boy or his father?
This is all about NLP introduction. To know how can we convert text data into vectors to perform Machine Learning Classification algorithms. You can now do programming in python by doing Text processing. Here I already wrote an article for it. Please visit.
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