What is Natural Language Processing?

February 24, 2023

Open up a new Google window and type in your favorite food. Did you notice? Before you even finished typing, Google probably already finished the phrase for you, or suggested some restaurants or recipes for you to try next. How? The answer: through natural language processing (NLP).

A bridge between the fields of linguistics and artificial intelligence, NLP essentially teaches computers to understand and produce human language. This article will serve as a brief introduction to the applications of and technology behind natural language processing. To learn more, enroll in our course, and you’ll learn speech and entity tagging on your own!

NLP plays an influential role in many aspects of people’s technological lives. One application of NLP lies in sentiment analysis, which determines the tone and underlying connotations of text. Companies often take advantage of sentiment analysis to understand customer feedback to better their products or services. In addition, social media platforms employ sentiment analysis to monitor comments.

After learning to converse in human languages, machine learning models can also serve as chatbots. This can save companies time by aiding with customer service through chatting with customers online or speaking over the phone! NLP probably works to save you time, too, as autocorrect and plagiarism detection such as through Grammarly are all applications of NLP.

Applications of Natural Language Processing | Data Science Dojo
Here are some of the many applications of NLP Image source

To teach a computer to do all this, though, first requires from the computer a fundamental understanding of human language. So where does a computer learn the components of language? Through data: paragraphs, sentences, phrases, and lots and lots of words.

The first step in training the model is segmentation: we break the sentences into phrases and words to individually process. Next, tokenization: we represent each word with a token and remove unimportant words such as “the” and “is” — known as stop words. Now, lemmatization: we group together words into their base root word. For example, the verbs “coding,” “codes,” and “coded” all hold the same meaning as the base word “code.”

Here’s where data annotation comes in: we now tag each word with its part of speech (speech tagging) and proper nouns with their qualities (named entity tagging). To practice doing this, check out our course! Finally, we can train the model using algorithms such as Naive Bayes to teach the computer simple grammar and phrase structure rules.

The Use of Natural Language Processing Approach for Converting Pseudo Code  to C# Code
An example of an approach to NLP through C# Image source
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