Natural Language Processing in a Big Data World NLP Sentiment Analysis

Cutting edge applications of natural language processing

examples of natural language processing

Ideally, your NLU solution should be able to create a highly developed interdependent network of data and responses, allowing insights to automatically trigger actions. Let’s take an example of how you could lower call center costs and improve customer satisfaction using NLU-based technology. Using our example, an unsophisticated software tool could respond by showing data for all types of transport, and display timetable information rather than links for purchasing tickets.

The Google Brain team uses a new concept called Switch Transformer that simplifies and improves previous approaches. In short, Switch Transformers aim to maximize parameter numbers in a computationally efficient way. Google Brain found they can scale and test out stable models up to 1.6 trillion parameters without any severe instability. As Google can now understand the context and intent of search queries, marketers need to ensure they deliver content that is highly relevant to target audiences. When it comes to natural language, online content now needs to be written for people’s benefit and not for search engines. With voice and mobile search growing, people want accurate and fast answers to their questions.

Named Entity Recognition

To better understand this stage of NLP, we have to broaden the picture to include the study of linguistics. However, even we humans find it challenging to receive, interpret, and respond to the overwhelming amount of language data we experience on a daily basis. Once your NLP tool has done its work and structured your data into coherent layers, the next step is to analyze that data.

examples of natural language processing

Failure to comply with regulations can result in serious consequences, including hefty fines, loss of business reputation, and even criminal charges. With the help of NLP, companies in the maritime industry can automate and streamline the regulatory compliance process, making it easier to identify and address potential risks. Remember, NLP is a vast field, and this article only scratches the surface.


For example, JPMorgan Chase developed a program called COiN that uses NLP to analyze legal documents and extract important data, reducing the time and cost of manual review. In fact, the bank was able to reclaim 360,000 hours annually by using NLP to handle everyday tasks. Machine learning algorithms use annotated datasets to train models that can automatically examples of natural language processing identify sentence boundaries. These models learn to recognize patterns and features in the text that signal the end of one sentence and the beginning of another. Sometimes sentences can follow all the syntactical rules but don’t make semantical sense. These help the algorithms understand the tone, purpose, and intended meaning of language.

What is NLP and how is it used?

Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.

NLU tools should be able to tag and categorise the text they encounter appropriately. Linguistics (or rule-based techniques) consist of creating a set of rules and grammars that identify and understand phrases and relationships among words. These are developed by linguistic experts and are then deployed on the NLP platform. Natural language processing (NLP) is a type examples of natural language processing of artificial intelligence (AI) that enables computers to interpret and understand spoken and written human language. The ultimate goal of NLP is to build machines that can understand human language, using speech and language processing. The larger the dataset, the better the chance of an AI-generated sentence being legible and in the same context as human writing.


Text analytics is a type of natural language processing that turns text into data for analysis. Learn how organisations in banking, health care and life sciences, manufacturing and government are using text analytics to drive better customer experiences, reduce fraud and improve society. Your device activated when it heard you speak, understood the unspoken intent in the comment, executed an action and provided feedback in a well-formed English sentence, all in the space of about five seconds. The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning. Some market research tools also use sentiment analysis to identify what customers feel about a product or aspects of their products and services.

examples of natural language processing

Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing has the ability to interrogate the data with natural language text or voice. This is also called “language in.” Most consumers have probably interacted with NLP without realizing it. For instance, NLP is the core technology behind virtual assistants, such as the Oracle Digital Assistant (ODA), Siri, Cortana, or Alexa. When we ask questions of these virtual assistants, NLP is what enables them to not only understand the user’s request, but to also respond in natural language. NLP applies both to written text and speech, and can be applied to all human languages.

What is NLP with example in AI?

What is natural language processing? Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.


Share on facebook
Share on linkedin
Share on email

Mais Vistos