Natural Language Processing NLP with Python Tutorial

What is Natural Language Processing? Definition and Examples

example of natural language

NLP uses artificial intelligence and machine learning, along with computational linguistics, to process text and voice data, derive meaning, figure out intent and sentiment, and form a response. As we’ll see, the applications of natural language processing are vast and numerous. Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront.

While tokenizing allows you to identify words and sentences, chunking allows you to identify phrases. Some sources also include the category articles (like “a” or “the”) in the list of parts of speech, but other sources consider them to be adjectives. Part of speech is a grammatical term that deals with the roles example of natural language words play when you use them together in sentences. Tagging parts of speech, or POS tagging, is the task of labeling the words in your text according to their part of speech. Fortunately, you have some other ways to reduce words to their core meaning, such as lemmatizing, which you’ll see later in this tutorial.

Natural Language Processing (NLP)

This difference means that, traditionally, it’s hard for computers to understand human language. Natural language processing aims to improve the way computers understand human text and speech. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations. One example is smarter visual encodings, offering up the best visualization for the right task based on the semantics of the data. This opens up more opportunities for people to explore their data using natural language statements or question fragments made up of several keywords that can be interpreted and assigned a meaning.

Whether it’s through Siri, Alexa, Google Assistant or other similar technology, many of us use these NLP-powered devices. There are, of course, far more steps involved in each of these processes. A great deal of linguistic knowledge is required, as well as programming, algorithms, and statistics. However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them.

Named Entity Recognition (NER):

But how would NLTK handle tagging the parts of speech in a text that is basically gibberish? Jabberwocky is a nonsense poem that doesn’t technically mean much but is still written in a way that can convey some kind of meaning to English speakers. So, ‘I’ and ‘not’ can be important parts of a sentence, but it depends on what you’re trying to learn from that sentence. If you want to integrate tools with your existing tools, most of these tools offer NLP APIs in Python (requiring you to enter a few lines of code) and integrations with apps you use every day. For example, NPS surveys are often used to measure customer satisfaction.

example of natural language

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