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Home » Software development » What is natural language processing with examples?

NLP offers exact answers to the question means it does not offer unnecessary and unwanted information. 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. Case Grammar was developed by Linguist Charles J. Fillmore in the year 1968. Case Grammar uses languages such as English to express the relationship between nouns and verbs by using the preposition. TextBlob is a Python library with a simple interface to perform a variety of NLP tasks.

natural language processing examples

Another tool enabled by natural language processing is SignAll that converts sign language into text. This can help individuals who are deaf communicate with those who don’t know sign language. 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. It mainly focuses on the literal meaning of words, phrases, and sentences.

Optimizing Contract Processes

Find out what else is possible with a combination of natural language processing and machine learning. Text analytics is a type of natural language processing that turns text into data for analysis. Learn how organizations in banking, health care and life sciences, manufacturing and government are using text analytics to drive better customer experiences, reduce fraud and improve society. The monolingual based approach is also far more scalable, as Facebook’s models are able to translate from Thai to Lao or Nepali to Assamese as easily as they would translate between those languages and English. As the number of supported languages increases, the number of language pairs would become unmanageable if each language pair had to be developed and maintained.

  • One example is smarter visual encodings, offering up the best visualization for the right task based on the semantics of the data.
  • Topic classification consists of identifying the main themes or topics within a text and assigning predefined tags.
  • NLP tools process data in real time, 24/7, and apply the same criteria to all your data, so you can ensure the results you receive are accurate – and not riddled with inconsistencies.
  • In 1957, Chomsky also introduced the idea of Generative Grammar, which is rule based descriptions of syntactic structures.
  • The Cloud NLP API is used to improve the capabilities of the application using natural language processing technology.

More than a mere tool of convenience, it’s driving serious technological breakthroughs. This application is increasingly important as the amount of unstructured data produced continues to grow. It is able to complete a range of functions from modelling risk management to processing unstructured data. Health Fidelity uses NLP to identify find structure in unstructured data. Meanwhile, Health Fidelity is providing natural language processing software to identify cases of fraud in the healthcare sector.

Virtual Assistants

Social media is one of the most important tools to gain what and how users are responding to a brand. Therefore, it is considered also one of the best natural language processing examples. And this is not the end, there is a list of natural language processing applications in the market, and more are about to enter the domain for better services.

natural language processing examples

Both solutions are capable of speeding up and optimizing claims processing. IBMs text mining software Watson Explorer and Taiger are both NLP driven solutions to the insurance industry. By continuing to monitor the use of a drug, the company is able to gather information on its side effects. For the financial sector NLPs ability to reduce risk and improve risk models may prove invaluable.


Natural language processing has been around for years but is often taken for granted. Here are eight examples of applications of natural language processing which you may not know about. If you have a large amount of text data, don’t hesitate to hire an NLP consultant such as Fast Data Science.

natural language processing examples

Using Waston Assistant, businesses can create natural language processing applications that can understand customer and employee languages while reverting back to a human-like conversation manner. What comes naturally to humans is challenging for computers in terms of unstructured data, absence of real-word intent, or maybe lack of formal rules. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. IBM has innovated in the AI space by pioneering NLP-driven tools and services that enable organizations to automate their complex business processes while gaining essential business insights.

Morphological and Lexical Analysis

As these examples of natural language processing showed, if you’re looking for a platform to bring NLP advantages to your business, you need a solution that can understand video content analysis, semantics, and sentiment mining. Natural language processing, the means by which machines interpret human speech and writing, is already embedded across everyday life. At a consumer level, natural language processing natural language processing in action enables machine translation services, GPS systems, transcription software, and customer service chatbots. At an enterprise level, businesses are using it to improve the employee experience, streamline everyday processes, and monitor employee sentiment with surveys. Natural language processing is an aspect of artificial intelligence that analyzes data to gain a greater understanding of natural human language.

This is increasingly important in medicine and healthcare, where NLP helps analyze notes and text in electronic health records that would otherwise be inaccessible for study when seeking to improve care or protect patient privacy. NLP plays a major role in numerous business processes, including sentiment analysis, language modeling, text summarization, and speech recognition. Because organizations continue to receive higher and higher volumes of data, technology that helps process large amounts of information is critical.

Reinforcement Learning

Other interesting applications of NLP revolve around customer service automation. This concept uses AI-based technology to eliminate or reduce routine manual tasks in customer support, saving agents valuable time, and making processes more efficient. Many natural language processing tasks involve syntactic and semantic analysis, used to break down human language into machine-readable chunks. Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks.

Learn how these insights helped them increase productivity, customer loyalty, and sales revenue. Repustate has helped organizations worldwide turn their data into actionable insights. There’s also some evidence that so-called “recommender systems,” which are often assisted by NLP technology, may exacerbate the digital siloing effect. This development is essentially a lie detector test for the written word. Computer scientists behind this software claim that is able to operate with 91% accuracy.

Natural language processing tools

As this application develops, alongside other smart driving solutions NLP will be key to features such as the virtual valet. NLP tools will allow physicians to dictate automatically to the EHR during patient consultations. COIN is able to process documents, highlighting and extracting certain words or phrases. These insights are presented in the form of dashboard notifications, helping the bank to create a personal connection with a customer. Natural language processing is also driving Question-Answering systems, as seen in Siri and Google. As the amount of online information continues to grow, the ability to easily access information in a foreign language grows in importance.

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