Natural Language Processing NLP: What it is and why it matters

Natural Language Processing can be applied into various areas like Machine Translation, Email Spam detection, Information Extraction, Summarization, Question Answering etc. Next, we discuss some of the areas with the relevant work done in those directions. To generate a text, we need to have a speaker or an application and a generator or a program that renders the application’s intentions into a fluent phrase relevant to the situation. NLP can be classified into two parts i.e., Natural Language Understanding and Natural Language Generation which evolves the task to understand and generate the text.

Every author has a characteristic fingerprint of their writing style – even if we are talking about word-processed documents and handwriting is not available. Natural language processing provides us with a set of tools to automate this kind of task. When companies have large amounts of text documents (imagine a law firm’s case load, or regulatory documents in a pharma company), it can be tricky to get insights out of it.

Examples of NLP:

Delivering the best customer experience and staying compliant with financial industry regulations can be driven through conversation analytics. Conversation analytics provides business insights that lead to better patient outcomes for the professionals in the healthcare industry. Improve quality and safety, identify competitive threats, and evaluate innovation opportunities. Looking ahead to the future of AI, two emergent areas of research are poised to keep pushing the field further by making LLM models more autonomous and extending their capabilities. Voice recognition, or speech-to-text, converts spoken language into written text; speech synthesis, or text-to-speech, does the reverse. These technologies enable hands-free interaction with devices and improved accessibility for individuals with disabilities.

This strategy lead them to increase team productivity, boost audience engagement and grow positive brand sentiment. The basketball team realized numerical social metrics were not enough to gauge audience behavior and brand sentiment. They wanted a more nuanced understanding of their brand presence to build a more compelling social media strategy.

Help Is Needed To Sift Through Data…and More Data

Then in 2017, Vaswani et al introduced the transformer architecture in a paper called “Attention is All You Need”, which was yet another quantum leap in the field. Transformers have fuelled the recent explosion in large language models (LLMs) such as ChatGPT. By the 2000s, large amounts of text data were widely available and companies such as Google were able to build large-scale statistical translation models. A sophisticated NLU solution should be able to rely on a comprehensive bank of data and analysis to help it recognize entities and the relationships between them. It should be able  to understand complex sentiment and pull out emotion, effort, intent, motive, intensity, and more easily, and make inferences and suggestions as a result. It should also have training and continuous learning capabilities built in.

Sprout Social’s Tagging feature is another prime example of how NLP enables AI marketing. Tags enable brands to manage tons of social posts and comments by filtering content. They are used to group and categorize social posts and audience messages based on workflows, business objectives and marketing strategies.

NLP Example for Language Identification

NLP can be used to analyze text data to determine the sentiment of the writer toward a particular product, service or brand. This is used in applications such as social media monitoring, customer feedback analysis and market research. Specifically, this article looks at sentiment analysis, chatbots, machine translation, text summarization and speech recognition as five instances of NLP in use in the real world. These applications have the potential to revolutionize the way one communicates with technology, making it more natural, intuitive and user-friendly. NLP has recently been incorporated into a number of practical applications, including sentiment analysis, chatbots and speech recognition. NLP is being used by businesses in a wide range of sectors to automate customer care systems, increase marketing initiatives and improve product offers.

If we find out what makes Google Maps or Apple’s Siri such incredible tools, we could also implement this technology into our business processes. The secret is not complicated and lies in a unique technology called Natural Language Processing (NLP). Google Maps and Siri are the two great natural language processing examples that help much with our daily routines.

Solutions for Product Management

For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation.

It is a strong contender in the use and application of Machine Learning, Artificial Intelligence and NLP. It enables organisations to work smarter, faster and with greater accuracy. The advanced features of the app can analyse speech from dialogue, team meetings, interviews, conferences and more. If you publish just a few pieces a month and need a quick summary, this might be a useful tool. But this isn’t the text analytics tool for scaling your content or summarizing a lot at once.

NLP in financial services at American Express

Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront.

Once search makes sense, however, it will result in increased revenue, customer lifetime value, and brand loyalty. Despite the impressive advancements in NLP technology, there are still many challenges to overcome. Words and phrases can have multiple meanings depending on context, tone, and cultural references. NLP algorithms must be trained to recognize and interpret these nuances if they are to accurately understand human language. In our globalized economy, the ability to quickly and accurately translate text from one language to another has become increasingly important.

Watson Oncology analyzes a patient’s medical records and pertinent data using natural language processing, assisting doctors in choosing the most appropriate course of therapy. It finds possible new applications for already-approved medications, accelerating the development of new drugs by evaluating vast amounts of scientific literature and research articles. Google has employed computer learning extensively to hone its search results.

SAS analytics solutions transform data into intelligence, inspiring customers around the world to make bold new discoveries that drive progress. Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, you’ve done these tasks manually before. Indeed, programmers used punch cards to communicate with the first computers 70 years ago. This manual and arduous process was understood by a relatively small number of people. Now you can say, “Alexa, I like this song,” and a device playing music in your home will lower the volume and reply, “OK.

As natural language processing continues to become more and more savvy, our big data capabilities can only become more and more sophisticated. Semantic search refers to a search method that aims to not only find keywords but understand the context of the search query and suggest fitting responses. Retailers claim that on average, e-commerce sites with a semantic search bar experience a mere 2% cart abandonment rate, compared to the 40% rate on sites with non-semantic search. As companies and individuals become increasingly globalized, effortless, and smooth communication is a business essential. Currently, more than 100 million people speak 12 different languages worldwide.

• By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans.
• It revolves around streamlining processes, revealing valuable insights, and engaging participants.
• There are a wide range of additional business use cases for NLP, from customer service applications (such as automated support and chatbots) to user experience improvements (for example, website search and content curation).
• Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection.

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• This helped them keep a pulse on campus conversations to maintain brand health and ensure they never missed an opportunity to interact with their audience.
• Microsoft has explored the possibilities of machine translation with Microsoft Translator, which translates written and spoken sentences across various formats.
• For example, the word “bank” could refer to a financial institution or the side of a river.
• In fact, MT/NLP research almost died in 1966 according to the ALPAC report, which concluded that MT is going nowhere.
• As just one example, brand sentiment analysis is one of the top use cases for NLP in business.