10 Examples of Natural Language Processing in Action

Normalization is useful in reducing the number of unique tokens present in the text, removing the variations of a word in the text, and removing redundant information too. Popular methods which are used for normalization are Stemming and Lemmatization. In the field of linguistics and NLP, a Morpheme is defined as the base form of a word. A token is generally made up of two components, Morphemes, which are the base form of the word, and Inflectional forms, which are essentially the suffixes and prefixes added to morphemes. Tokenization can be performed at the sentence level or at the world level or even at the character level. Notice that “New-York” is not split further because the tokenization process was based on whitespaces only.

Search engines 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. POS stands for parts of speech, which includes Noun, verb, adverb, and Adjective. It indicates that how a word functions with its meaning as well as grammatically within the sentences.

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By tokenizing a book into words, it’s sometimes hard to infer meaningful information. Chunking literally means a group of words, which breaks simple text into phrases that are more meaningful than individual words. In English and many other languages, a single word can take multiple forms depending upon context used. For instance, the verb “study” can take many forms like “studies,” “studying,” “studied,” and others, depending on its context. When we tokenize words, an interpreter considers these input words as different words even though their underlying meaning is the same.

It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on. NLU mainly used in Business applications to understand the customer’s problem in both spoken and written language. LUNAR is the classic example of a Natural Language database interface system that is used ATNs and Woods’ Procedural Semantics. It was capable of translating elaborate natural language expressions into database queries and handle 78% of requests without errors. Automatic summarization can be particularly useful for data entry, where relevant information is extracted from a product description, for example, and automatically entered into a database.

Top-notch Examples of Natural Language Processing in Action

Or been to a foreign country and used a digital language translator to help you communicate? How about watching a YouTube video with captions, which were likely created using Caption Generation? These are just a few in action and how this technology impacts our lives. 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.

To do that, the app has to be taught to understand the accent and language patterns of a given celebrity to generate believable language. Like all GPS apps, it comes with a standard female voice that guides you as you drive. But you can also download voice packs of famous people like Arnold Schwarzenegger and Mr. T to make your drive just a bit more entertaining. Of course, you can use it to check for content gaps or opportunities to expand single pieces of content into clusters.

NLP Example for Sentiment Analysis

Such a fiasco could lead to identity theft for your customer, and stiff penalties, class action suits, and PR nightmares for your company. Companies are offering more communication channels, where customers provide sensitive information like their contact info, birthdates, and payment account numbers. Hackers are finding more opportunities to decrypt and sell customer data. New developments in privacy-preserving NLP mean that it will soon be possible to remove sensitive customer data from all records, even in the context of recorded customer service conversations. Plus, a chatbot powered by NLP can provide necessary backgrounds and details to a human agent at handoff, so the customer doesn’t have to repeat it, and the agent won’t have to spend time searching through records. It’s a nightmare for customers with complicated issues to explain their problem to a chatbot, then an agent, then their supervisor, then a specialist before finally getting a resolution.

• NLP has been used by IBM Watson, a top AI platform, to enhance healthcare results.
• They then learn on the job, storing information and context to strengthen their future responses.
• Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few.
• The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
• Next, we are going to remove the punctuation marks as they are not very useful for us.

Natural language processing is behind the scenes for several things you may take for granted every day. When you ask Siri for directions or to send a text, natural language processing enables that functionality. Arguably one of the most well known examples of NLP, smart assistants have become increasingly integrated into our lives. Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices. Now, thanks to AI and NLP, algorithms can be trained on text in different languages, making it possible to produce the equivalent meaning in another language.

Natural Language Processing with Python

Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents. Google Translate, Microsoft Translator, and Facebook Translation App are a few of the leading platforms for generic machine translation. In August 2019, Facebook AI English-to-German machine translation model received first place in the contest held by the Conference of Machine Learning (WMT). The translations obtained by this model were defined by the organizers as “superhuman” and considered highly superior to the ones performed by human experts.

The Snowball stemmer, which is also called Porter2, is an improvement on the original and is also available through NLTK, so you can use that one in your own projects. It’s also worth noting that the purpose of the Porter stemmer is not to produce complete words but to find variant forms of a word. Stemming is a text processing task in which you reduce words to their root, which is the core part of a word. For example, the words “helping” and “helper” share the root “help.” Stemming allows you to zero in on the basic meaning of a word rather than all the details of how it’s being used. NLTK has more than one stemmer, but you’ll be using the Porter stemmer.

What is the most difficult part of natural language processing?

If a particular word appears multiple times in a document, then it might have higher importance than the other words that appear fewer times (TF). At the same time, if a particular word appears many times in a document, but it is also present many times in some other documents, then maybe that word is frequent, so we cannot assign much importance to it. For instance, we have a database of thousands of dog descriptions, and the user wants to search for “a cute dog” from our database.

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