Basics of Natural Language Processing (NLP)
The ultimate guide to know the basics of Natural Language Processing
What is Natural Language Processing?
Can we teach computers to recognize words and sentences? One of the objectives is to match or exceed essential human skills. Language is one of such talents (communication, knowing the meaning of something, arriving at conclusions based on the words and sentences).
This is where Natural Language Processing (NLP) enters the picture. It is a subfield of artificial intelligence that focuses on comprehending and interpreting human language. It can include comprehension and interpretation of both text and speech.
Why Natural Language Processing (NLP)?
You must have used Google’s voice search? Are you familiar with chatbots (which react automatically depending on your questions and words)? How about using Google Translate? Have you ever communicated with an AI customer service system?
Natural Language Processing (NLP) is in action. In reality, the NLP business might become a worth billions of dollars sector within a few or few years. This is due to the fact that it has the potential to be widely employed in customer service, the production of virtual assistants (akin to Iron Man’s JARVIS), healthcare paperwork, and other sectors.
Natural Language Processing is also used to comprehend and gauge feelings in social media postings, blogs, customer reviews, news, and other online sources. Because of the large amount of data available from internet activity, NLP is extremely effective in these domains. Remember that if we have enough high-quality data to work with, we can dramatically enhance our data analysis and machine learning models.
Analyzing Words & Sentiments
Understanding the sentiment in a piece of text (e.g., is it a favorable or bad product review?) is among the most popular applications of NLP. What does the tweet say in total?). We don’t need technology to read a dozen comments and evaluations. But what if we had hundreds or thousands of phrases to read?
Inside this substantial endeavor, technology is extremely beneficial. Adopting NLP can make the process easier and our findings more consistent and reproducible. Let’s look at the example.
To get started, let’s study Restaurant_Reviews.tsv (let’s take a peek):
Wow… Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
Stopped by during the late May bank holiday off Rick Steve recommendation and loved it. 1
The selection on the menu was great and so were the prices. 1
Now I am getting angry and I want my damn pho. 0
Honeslty it didn’t taste THAT fresh.) 0
The potatoes were like rubber and you could tell they had been made up ahead of time being kept under a warmer. 0
The fries were great too. 1
A great touch. 1
Service was very prompt. 1
Would not go back. 0
The cashier had no care what so ever on what I had to say it still ended up being wayyy overpriced. 0
I tried the Cape Cod ravoli, chicken, with cranberry…mmmm! 1
I was disgusted because I was pretty sure that was human hair. 0
I was shocked because no signs indicate cash only. 0
Highly recommended. 1
Waitress was a little slow in service. 0
This place is not worth your time, let alone Vegas. 0
did not like at all. 0
The Burrittos Blah! 0
The first element is a statement in which a person expresses his or her opinion or experience with the eatery. The second component is whether or not the statement is negative (0 if negative, 1 if positive/Liked). This is quite similar to Supervised Learning, where labels are applied early on.
However, NLP is distinct in that we are mostly working with text and language rather than numerical data. Understanding text (for example, recognizing similarities and inferring rules) may also be difficult. This is because language is frequently inconsistent and lacks specific norms. For example, altering, removing, or adding a few words in a phrase might radically alter its meaning.
There’s also the issue of context, which affects how words are employed and hence their meaning. We also have to deal with “filler” words, which are merely there to complete the phrase but have no significance.
Evaluating sentences, identifying meaning, and detecting the writer’s emotions might be difficult. As a result, even experienced programmers find it challenging to come up with a solution for dealing with words and language.
Natural Language Toolkit (NLTK)
Thankfully, there are now libraries and applications available that make Natural Language Processing accessible to even novice programmers and practitioners. The Natural Language Toolkit is one of the most popular packages (NLTK).
Text processing is easier using NLTK (created by Steven Bird and Edward Loper at the Department of Computer and Information Science at the University of Pennsylvania.) since you’ll be implementing pre-built code rather than coding everything from scratch. In reality, several nations and colleges include NLTK in their curriculum.
Conclusion
- First, we learned about the communication barrier between humans and computers, as well as the difficulties it produces.
- There’s the immense complexity and variety of linguistics, which presents still another set of obstacles.
- Natural Language Processing is also used to comprehend and gauge feelings in social media postings, blogs, customer reviews, news, and other online sources.
- However, NLP is distinct in that we are mostly working with text and language rather than numerical data.
- Natural Language Processing has a library called NLTK, which makes text processing much easier.
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