NLP Techniques

NLP Techniques

Hello everyone, I hope you’re having a perfect day.  

So tell me if you have heard somewhere about NLP.  

It might seem not very clear sometimes because you, unfortunately, find two definitions for NLP on Google.  

The first is “Neuro-Linguistic Programming,” and the second one is “Natural Language Processing.”  

Okay, today we are here to talk about “Natural Language Processing,” but you must know the difference between them.  

NLP Vs. NLP  

Natural language processing  

The heading itself says that it’s something about artificial intelligence, a subfield of linguistics. It is an interaction between computers and humans to process a large amount of data.  

Speech recognition would be the best example of this. It is the process by which the computer interacts with a human’s natural voice/ language and recognizes it to show relevant results as done in voice typing or searching through a mic instead of typing.  

Neuro linguistic programming  

As per my understanding, it is observing and correcting the patterns, models, and physiology we are using. You can even manipulate it to something more effective to get success at an early stage.  

Though both the NLPs sound very similar but they are not.  

Neuro-linguistic programming refers to a tool that allows you to communicate effectively with yourself and those around you.  

Natural language processing is a feature to help you talk to machines. So both the tools follow a different procedure.  

Detailed information about Natural Language Processing  

So far, we have discussed the basics of NLP. Now let’s go a bit deeper.  

As you read earlier, the NLP (Natural Language Processing) is a component of artificial intelligence. I would love to inform you that it has existed for more than 50 years and has many real-world applications.  

NLP Techniques 

While reading about any topic, it is advised to get complete information about it. So, to explain to you easily about its working, we have provided the techniques that the NLP uses.  

Given below are some top NLP techniques which will help you a lot if you belong to the field of a data scientist:  

1. Tokenization  

It is one of the essential techniques while doing NLP.  

A token is basically referred to as the small units of texts made by breaking the long-running text strings.  

2. Stemming and Lemmatization  

It is the second most step while performing an NLP. This is a basic method to analyze the meaning of any word. They have been widely used for SEO, web search results, and tagging works.  

3. Stop words removal  

It is not as such important to be done but is helpful when any text needs to be classified into different categories.It removes unwanted, useless words that are generally used only to connect them to a complete sentence.  

4. TF-IDF  

Fair TF (term frequency) and IDF (inverse document frequency) are combined to find essential words in a document. Such as the words with the high frequency but within the specific Corpus.  

5. Keyword extraction  

Finding the keyboard from a text with a wide range of words is not an easy task. So, the keyword extraction step makes it easy as it says the time of the reader and highlights the essential words in a sentence that explain the whole document in a short view.  

6. Word embedding  

Word meaning in step process where the words are converted foreign numerical representation. These are represented in a way that the words with similar meanings appear close to each other.  

7. Sentiment analysis  

This type of analysis is done to identify the category of text such as tweets, news articles, reviews, etc., and mitigate them to prevent any distress to the customers.  

8. Topic modeling  

It is also a good step toward excellent extensive data and summarises it to make it easy to understand.  

9. Text summarisation  

This step is to summarize the text for easier extraction of information without reading every word of the document.  

10. Named entity recognition  

Riddles identifying the named identities according to their redefined categories such as person’s name, address, date, etc., from any raw document.  

The above NLP techniques would probably help you in your data work.  

We’ll be back with our next article soon. Till then you can follow the techniques, we suggested for good progress.  

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