Natural Language Processing NLP Examples

12 Real-World Examples Of Natural Language Processing NLP

natural language examples

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. So, ‘I’ and ‘not’ can be important parts of a sentence, but it depends on what you’re trying to learn from that sentence. You iterated over words_in_quote with a for loop and added all the words that weren’t stop words to filtered_list. You used .casefold() on word so you could ignore whether the letters in word were uppercase or lowercase. This is worth doing because stopwords.words(‘english’) includes only lowercase versions of stop words.

Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it. When we think about the importance of NLP, it’s worth considering how human language is structured. As well as the vocabulary, syntax, and grammar that make written sentences, there is also the phonetics, tones, accents, and diction of spoken languages. With the recent focus on large language models (LLMs), AI technology in the language domain, which includes NLP, is now benefiting similarly. You may not realize it, but there are countless real-world examples of NLP techniques that impact our everyday lives.

Real-World Examples of AI Natural Language Processing

This is done by using NLP to understand what the customer needs based on the language they are using. This is then combined with deep learning technology to execute the no longer just use keywords to help users reach their search results. They now analyze people’s intent when they search for information through NLP. Through context they can also improve the results that they show.

You can use is_stop to identify the stop words and remove them through below code.. As we already established, when performing frequency analysis, stop words need to be removed. The process of extracting tokens from a text file/document is referred as tokenization. The words of a text document/file separated by spaces and punctuation are called as tokens. It supports the NLP tasks like Word Embedding, text summarization and many others.

What is Extractive Text Summarization

Then, using text-to-speech translations with natural language generation (NLG) algorithms, they reply with the most relevant information. Natural language processing (NLP ) is a type of artificial intelligence that derives meaning from human language in a bid to make decisions using the information. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications.

Furthermore, if you conduct consumer surveys, you can gain decision-making insights on products, services, and marketing budgets. Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed-up their writing process and correct common typos. The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence.

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SignAll is another tool that is natural language processing-powered. For instance, when you request Siri to give you directions, it is natural language processing technology that facilitates that functionality. Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products.

natural language examples

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