14 Natural Language Processing Examples NLP Examples

Government agencies are bombarded with text-based data, including digital and paper documents. With its AI and NLP services, Maruti Techlabs allows businesses to apply personalized searches to large data sets. 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. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time.

Data cleaning techniques are essential to getting accurate results when you analyze data for various purposes, such as customer experience insights, brand monitoring, market research, or measuring employee satisfaction. Consider that former Google chief Eric Schmidt expects general artificial intelligence in 10–20 years and that the UK recently took an official position on risks from artificial general intelligence. Had organizations paid attention to Anthony Fauci’s 2017 warning on the importance of pandemic preparedness, the most severe effects of the pandemic and ensuing supply chain crisis may have been avoided. However, unlike the supply chain crisis, societal changes from transformative AI will likely be irreversible and could even continue to accelerate. Organizations should begin preparing now not only to capitalize on transformative AI, but to do their part to avoid undesirable futures and ensure that advanced AI is used to equitably benefit society. The bottom line is that you need to encourage broad adoption of language-based AI tools throughout your business.

What is Natural Language Processing? Definition and Examples

IBM equips businesses with the Watson Language Translator to quickly translate content into various languages with global audiences in mind. With glossary and phrase rules, companies are able to customize this AI-based tool to fit the market and context they’re targeting. Machine examples of natural language processing learning and natural language processing technology also enable IBM’s Watson Language Translator to convert spoken sentences into text, making communication that much easier. Organizations and potential customers can then interact through the most convenient language and format.

• MIT News reported in 2008 that Weizenbaum “grew skeptical of AI” and “was shocked to discover that many users were taking his program seriously and were opening their hearts to it.”
• Users can describe intended properties such as materials, size and weight.
• Computers and machines are great at working with tabular data or spreadsheets.
• Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications.
• The COPD Foundation uses text analytics and sentiment analysis, NLP techniques, to turn unstructured data into valuable insights.

I hope you can now efficiently perform these tasks on any real dataset. For example, let us have you have a tourism company.Every time a customer has a question, you many not have people to answer. The concept is based on capturing the meaning of the text and generating entitrely new sentences to best represent them in the summary.

Why is Natural Language Processing Important?

As technology evolves, we can expect these applications to become even more integral to our daily interactions, making our experiences smoother and more intuitive. If you used a tool to translate it instantly, you’ve engaged with Natural Language Processing. As we delve into specific Natural Language Processing examples, you’ll see firsthand the diverse and impactful ways NLP shapes our digital experiences.

But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do. One of the most interesting applications of NLP is in the field of content marketing. AI-powered content marketing and SEO platforms like Scalenut help marketers create high-quality content on the back of NLP techniques like named entity recognition, semantics, syntax, and big-data analysis. Enterprise communication channels and data storage solutions that use natural language processing (NLP) help keep a real-time scan of all the information for malware and high-risk employee behavior. If users are unable to do something, the goal is to help them do it.

Real-World Examples Of Natural Language Processing (NLP) In Action

The goal of NLP is to bridge the communication gap between humans and computers. NLP models are typically trained on tasks such as next word prediction which allow them to build contextual dependencies and then be able to generate relevant outputs. Controlled natural languages are subsets of natural languages whose grammars and dictionaries have been restricted in order to reduce ambiguity and complexity. This may be accomplished by decreasing usage of superlative or adverbial forms, or irregular verbs. Typical purposes for developing and implementing a controlled natural language are to aid understanding by non-native speakers or to ease computer processing. An example of a widely-used controlled natural language is Simplified Technical English, which was originally developed for aerospace and avionics industry manuals.

A broader concern is that training large models produces substantial greenhouse gas emissions. Natural language processing goes hand in hand with text analytics, which counts, groups and categorizes words to extract structure and meaning from large volumes of content. Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. Marketers are always looking for ways to analyze customers, and NLP helps them do so through market intelligence.

Begin incorporating new language-based AI tools for a variety of tasks to better understand their capabilities.

There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. ANNs are the basis of deep learning, which is the ability of the ANN to learn from large amounts of data.

However, this process can take much time, and it requires manual effort. As you can see in our classic set of examples above, it tags each statement with ‘sentiment’ then aggregates the sum of all the statements in a given dataset. Natural language processing, the deciphering of text and data by machines, has revolutionized data analytics across all industries.

See how we can help you create SEO content faster and better.

The challenge in this domain lies in the sheer volume of biomedical literature and the unique hurdles it presents for curation, interpretation, and knowledge extraction. For instance, biomedical literature databases like PubMed alone see an increase of ~5,000 articles every day, totaling over 36 million. In addition to volume, biomedical literature also poses domain-specific challenges. A single entity like Long COVID can be described using 763 different terms.

Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Predictive text and its cousin autocorrect have evolved a lot and now we have applications like Grammarly, which rely on natural language processing and machine learning. We also have Gmail’s Smart Compose which finishes your sentences for you as you type. However, large amounts of information are often impossible to analyze manually.

Benefits of natural language processing

The goal of a chatbot is to provide users with the information they need, when they need it, while reducing the need for live, human intervention. NLP is also a driving force behind programs designed to answer questions, often in support of customer service initiatives. Backed by AI, question answering platforms can also learn from each consumer interaction, which allows them to improve interactions over time. It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions.