5 real-world applications of natural language processing NLP

What is Natural Language Processing?

example of natural language processing

To learn more about how natural language can help you better visualize and explore your data, check out this webinar. Processing several candidates from social media sites can be time- consuming. With the help of natural language processing, this doesn’t have to be the case. Recruitment consultants can begin shortlisting candidates before they meet in person.

example of natural language processing

Finally, natural language processing uses machine learning methods to enhance language comprehension and interpretation over time. These algorithms let the system gain knowledge from previous encounters, improve functionality, and predict inputs in the future. “Question Answering (QA) is a research area that combines research from different fields, with a common subject, which are Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP). Actually, current search engine just do ‘document retrieval’, i.e. given some keywords it only returns the relevant ranked documents that contain these keywords. Hence QAS is designed to help people find specific answers to specific questions in restricted domain. NLP powers intelligent chatbots and virtual assistants—like Siri, Alexa, and Google Assistant—which can understand and respond to user commands in natural language.

NLP in agriculture: AgriTech

Her work includes the development and execution of content strategies for B2B and B2C companies, including marketing and audience research, content calendar creation, hiring and managing writers and editors, and SEO optimization. But the technology is getting better and better, and there are a variety of tools to help you accomplish exactly the kind of summarization you need. There are even chrome extensions that can help you out, though it might be hard to scale content summaries that way. It crawls individual pieces of content using NLP to flag thin content and suggests opportunities to deepen your topic coverage. It will even suggest subtopics to cover, as well as questions to answer and primary and secondary keywords to include.

  • Natural language processing can be used for topic modelling, where a corpus of unstructured text can be converted to a set of topics.
  • Query understanding and document understanding build the core of Google search.
  • They then learn on the job, storing information and context to strengthen their future responses.
  • Through projects like the Microsoft Cognitive Toolkit, Microsoft has continued to enhance its NLP-based translation services.

Whether reading text, comprehending its meaning, or generating human-like responses, NLP encompasses a wide range of tasks. Have you ever spoken to Siri or Alexa and marveled at their ability to understand and respond? The more you use predictive text, the more it will adapt to your unique speech patterns.

Natural language processing in business

For many businesses, the chatbot is a primary communication channel on the company website or app. It’s a way to provide always-on customer support, especially for frequently asked questions. Online translators are now powerful tools thanks to Natural Language Processing. If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations.

example of natural language processing

Using the NLP system can help in aggregating the information and making sense of each feedback and then turning them into valuable insights. This will not just help users but also improve the services rendered by the company. And there are many natural language processing examples that we all are using for the last many years.

Applications of Natural Language Processing: Reshaping Businesses with the Power of Automation

The role of chatbots in enterprise along with NLP lessens the need to enroll more staff for every customer. On the other hand, data that can be extracted from the machine is nearly impossible for employees for interpreting all the data. It is an effective and extremely convenient method to search or discover precise information.

Creating a perfect code frame is hard, but thematic analysis software makes the process much easier. Spam detection removes pages that match search keywords but do not provide the actual search answers. Auto-correct finds the right search keywords if you misspelled something, or used a less common name. In layman’s terms, a Query is your search term and a Document is a web page. Because we write them using our language, NLP is essential in making search work. Any time you type while composing a message or a search query, NLP helps you type faster.

A suite of NLP capabilities compiles data from multiple sources and refines this data to include only useful information, relying on techniques like semantic and pragmatic analyses. In addition, artificial neural networks can automate these processes by developing advanced linguistic models. Teams can then organize extensive data sets at a rapid pace and extract essential insights through NLP-driven searches. Overall, this will help your business offer personalized search results, product recommendations, and promotions to drive more revenue. By combination of machine learning and natural language processing, your brand can find an edge in a highly competitive and oversaturated market, scale your organization, and cut down on manual processes. NLP algorithms are designed to recognize patterns in human language and extract meaning from text or speech.


It then adds, removes, or replaces letters from the word, and matches it to a word candidate which fits the overall meaning of a sentence. Most of the time, there is a programmed answering machine on the other side. Although sometimes tedious, this allows corporations to filter customer information and quickly get you to the right representative.

Example of Natural Language Processing for Information Retrieval and Question Answering

By combining machine learning with natural language processing and text analytics. Find out how your unstructured data can be analyzed to identify issues, evaluate sentiment, detect emerging trends and spot hidden opportunities. NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. There are several NLP techniques that enable AI tools and devices to interact with and process human language in meaningful ways.

example of natural language processing

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