8 NLP Examples: Natural Language Processing in Everyday Life

Natural Language Definition and Examples

natural language examples

The type of data that can be “fed” to a large language model can include books, pages pulled from websites, newspaper articles, and other written documents that are human language-based. It is a method of extracting essential features from row text so that we can use it for machine learning models. We call it “Bag” of words because we discard the order of occurrences of words. A bag of words model converts the raw text into words, and it also counts the frequency for the words in the text. In summary, a bag of words is a collection of words that represent a sentence along with the word count where the order of occurrences is not relevant. In this article, we explore the basics of natural language processing (NLP) with code examples.

natural language examples

This is largely thanks to NLP mixed with ‘deep learning’ capability. Deep learning is a subfield of machine learning, which helps to decipher the user’s intent, words and sentences. This type of NLP looks at how individuals and groups of people use language and makes predictions about what word or phrase will appear next. The machine learning model will look at the probability of which word will appear next, and make a suggestion based on that.

Why is Natural Language Generation important?

Very common words like ‘in’, ‘is’, and ‘an’ are often used as stop words since they don’t add a lot of meaning to a text in and of themselves. We can use Wordnet to find meanings of words, synonyms, antonyms, and many other words. With lexical analysis, we divide a whole chunk of text into paragraphs, sentences, and words. For instance, the freezing temperature can lead to death, or hot coffee can burn people’s skin, along with other common sense reasoning tasks. However, this process can take much time, and it requires manual effort. An ontology class is a natural-language program that is not a concept in the sense as humans use concepts.

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Predictive text has become so ingrained in our day-to-day lives that we don’t often think about what is going on behind the scenes. As the name suggests, predictive text works by predicting what you are about to write. Over time, predictive text learns from you and the language you use to create a personal dictionary.

When the dataset that’s used for training is biased, that can then result in a large language model generating equally biased, inaccurate, or unfair responses. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur.

Natural Language Processing (NLP) with Python — Tutorial

Syntactic analysis involves the analysis of words in a sentence for grammar and arranging words in a manner that shows the relationship among the words. For instance, the sentence “The shop goes to the house” does not pass. In the sentence above, we can see that there are two “can” words, but both of them have different meanings.

The easiest way to get started with BERT is to install a library called Hugging Face. You can see that BERT was quite easily able to retrieve the facts (On August 26th, 1928, the Appellant https://www.metadialog.com/ drank a bottle of ginger beer, manufactured by the Respondent…). Although impressive, at present the sophistication of BERT is limited to finding the relevant passage of text.

Various Stemming Algorithms:

With named entity recognition, you can find the named entities in your texts and also determine what kind of named entity they are. These are some of the basics for the exciting field of natural language processing (NLP). We hope you enjoyed reading this article and learned something new.


Any time you type while composing a message or a search query, NLP helps you type faster. You must — there are over 200,000 words in our free online dictionary, but you are looking for one that’s only in the Merriam-Webster Unabridged Dictionary. NLG solutions can help to improve the communication of personalized plans for customers. Grammaticalization stage makes sure that the whole report follows the correct grammatical form, spelling, and punctuation. This includes validation of actual text according to the rules of syntax, morphology, and orthography.

Generating Content

With the help of entity resolution, “Georgia” can be resolved to the correct category, the country or the state. Gensim is an NLP Python framework generally used in topic modeling and similarity detection. It is not a general-purpose NLP library, but it handles tasks assigned to it very well.

natural language examples

By using Towards AI, you agree to our Privacy Policy, including our cookie policy. Notice that the first description contains 2 out of 3 words from our user query, and the second description contains 1 word from the query. The third description also contains 1 word, and the forth description contains no words from the user query. As we can sense that the closest answer to our query will be description number two, as it contains the essential word “cute” from the user’s query, this is how TF-IDF calculates the value. Lemmatization tries to achieve a similar base “stem” for a word. However, what makes it different is that it finds the dictionary word instead of truncating the original word.

Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral. For instance, if an unhappy client sends an email which mentions the terms “error” and “not worth the price”, then their opinion would be automatically natural language examples tagged as one with negative sentiment. Search engines leverage NLP to suggest relevant results based on previous search history behavior and user intent. Natural language processing (NLP) is a branch of Artificial Intelligence or AI, that falls under the umbrella of computer vision.

natural language examples

Also, for languages with more complicated morphologies than English, spellchecking can become very computationally intensive. Learning more about what large language models are designed to do can make it easier to understand this new technology and how it may impact day-to-day life now and in the years to come. Large language models (LLMs) are something the average person may not give much thought to, but that could change as they become more mainstream. For example, if you have a bank account, use a financial advisor to manage your money, or shop online, odds are you already have some experience with LLMs, though you may not realize it. Large language models primarily face challenges related to data risks, including the quality of the data that they use to learn. Biases are another potential challenge, as they can be present within the datasets that LLMs use to learn.

Next, we are going to use RegexpParser( ) to parse the grammar. Notice that we can also visualize the text with the .draw( ) function. As shown above, the final graph has many useful words that help us understand what our sample data is about, showing how essential it is to perform data cleaning on NLP. Next, we are going to remove the punctuation marks as they are not very useful for us. We are going to use isalpha( ) method to separate the punctuation marks from the actual text. Also, we are going to make a new list called words_no_punc, which will store the words in lower case but exclude the punctuation marks.

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When you use a concordance, you can see each time a word is used, along with its immediate context. This can give you a peek into how a word is being used at the sentence level and what words are used with it. While tokenizing allows you to identify words and sentences, chunking allows you to identify phrases. The Porter stemming algorithm dates from 1979, so it’s a little on the older side.

The saviors for students and professionals alike – autocomplete and autocorrect – are prime NLP application examples. Autocomplete (or sentence completion) integrates NLP with specific Machine learning algorithms to predict what words or sentences will come next, in an effort to natural language examples complete the meaning of the text. By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way. Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis.

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  • I hope you can now efficiently perform these tasks on any real dataset.
  • We are going to use isalpha( ) method to separate the punctuation marks from the actual text.

An NLP system can look for stopwords (small function words such as the, at, in) in a text, and compare with a list of known stopwords for many languages. The language with the most stopwords in the unknown text is identified as the language. So a document with many occurrences of le and la is likely to be French, for example. You would think that writing a spellchecker is as simple as assembling a list of all allowed words in a language, but the problem is far more complex than that. How can such a system distinguish between their, there and they’re? Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used.

natural language examples

NLP is not perfect, largely due to the ambiguity of human language. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. A direct word-for-word translation often doesn’t make sense, and many language translators must identify an input language as well as determine an output one. The science of identifying authorship from unknown texts is called forensic stylometry. Every author has a characteristic fingerprint of their writing style – even if we are talking about word-processed documents and handwriting is not available. A slightly more sophisticated technique for language identification is to assemble a list of N-grams, which are sequences of characters which have a characteristic frequency in each language.

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