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5 Real-world Applications Of Natural Language Processing (NLP)

Natural language processing (NLP) is a field of study that focuses on enabling computers to understand and interpret human language. NLP involves applying machine learning algorithms to analyze and process natural language data, such as text or speech.

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.

Related: 5 natural language processing (NLP) libraries to use

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.

Sentiment analysis

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.

A common use of NLP is sentiment analysis of the stock market, in which investors and traders examine social media sentiment on a particular stock or market. An investor, for instance, can use NLP to examine tweets or news stories about a specific stock to ascertain the general attitude of the market toward that stock. Investors can determine whether these sources are expressing positive or negative opinions about the stock by studying the terminology used in these sources.

By supplying information on market sentiment and enabling investors to modify their strategies as necessary, sentiment research can assist investors in making more educated investment decisions. For instance, if a stock is receiving a lot of positive sentiment, an investor may consider buying more shares, while negative sentiment may prompt them to sell or hold off on buying.

Chatbots

NLP can be used to build conversational interfaces for chatbots that can understand and respond to natural language queries. This is used in customer support systems, virtual assistants and other applications where human-like interaction is required.

A chatbot like ChatGPT that can help consumers with their account questions, transaction histories and other financial questions might be created by a financial institution using NLP. Customers can easily obtain the information they require thanks to the chatbot's ability to comprehend and respond to natural language questions.

Machine translation

NLP can be used to translate text from one language to another. This is used in applications such as Google Translate, Skype Translator and other language translation services.

Similarly, a multinational corporation may use NLP to translate product descriptions and marketing materials from their original language to the languages of their target markets. This allows them to communicate more effectively with customers in different regions.

Text summarization

NLP can be used to summarize long documents and articles into shorter, concise versions. This is used in applications such as news aggregation services, research paper summaries and other content curation services.

NLP can be used by a news aggregator to condense lengthy news stories into shorter, easier-to-read versions. Without having to read the entire article, readers can immediately receive a summary of the news thanks to text summarization.

Related: 7 artificial intelligence (AI) examples in everyday life

Speech recognition

NLP can be used to convert spoken language into text, allowing for voice-based interfaces and dictation. This is used in applications such as virtual assistants, speech-to-text transcription services and other voice-based applications.

A virtual assistant, such as Alexa from Amazon or Assistant from Google, uses NLP to comprehend spoken instructions and answer questions in natural language. Instead of having to type out commands or inquiries, users may now converse with the assistant by speaking.

A New Era Of Natural Language Search Emerges For The Enterprise

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Large language models and their applications are suddenly a hot topic due to the popular release of OpenAI's ChatGPT and the subsequent search engine wars between Google and Microsoft. ChatGPT and similar systems are revitalizing our idea of what the search experience can be. Instead of relying on specific keywords or complex search query syntax, users can now interact with search engines using human-like language.

Question answering systems are one capability of natural language processing, a set of language capabilities that LLMs enable, but QA systems have not always been a popular use case. Ryan Welsh, CEO of an NLP search company called Kyndi, recalls facing difficulties when explaining his company's focus on NLP for search: "I remember raising money three years ago and everyone was like, 'Hey, that's cool, you're an NLP, but this search thing is not a use case.'"

Welsh says this reaction has completely changed thanks to how many people are aware of natural language capabilities because of ChatGPT: "I feel like ChatGPT has done a decade's worth of category evangelism in 90-120 days," he said in an interview with Datanami.

Now, billions are being invested in next-generation search tech. There is suddenly a real market demand for QA systems that can give fast and accurate answers to questions asked by stakeholders or external customers visiting a company's website or knowledge portal, as well as for internal employees searching company documents.

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However, these current chatbot technologies are not meeting enterprise demands, Welsh says, noting that explainability, which is key for end-user trust, is often lacking. Enterprise requirements for a large language model system dictate that generated answers are accurate and trustworthy versus filled with hallucinations from training data sourced from web content, a problem that the large, mainstream models like ChatGPT face. Due to the statistical nature of their underlying technology, chatbots can hallucinate incorrect information, as they do not actually understand the language but are simply predicting the next best word. Often, the training data is so broad that explaining how a chatbot arrived at the answer it gave is nearly impossible.

This "black box" approach to AI with its lack of explainability simply will not fly for many enterprise use cases. Welsh gives the example of a pharmaceutical company that is delivering answers to a healthcare provider or a patient who visits its drug website. The company is required to know and explain each search result that could be given to those asking questions. So, despite the recent spike in demand for systems like ChatGPT, adapting them for these stringent enterprise requirements is not an easy task, and this demand is often unmet, according to Welsh.

Welsh says his company has stayed focused on these enterprise requirements over the years, learning from experience and direct interaction with customers. Kyndi was founded in 2014 by Welsh, AI expert Arun Majumbar, and computer scientist John Sowa, an expert in knowledge graphs who introduced a specific type called conceptual graphs at IBM in 1976.

Kyndi's natural language search application builds on both knowledge graph and LLM breakthroughs by employing neuro-symbolic AI, a semantic approach that complements statistical machine learning techniques. Instead of just predicting the next most-likely word in text, the system creates symbolic representations of language, leveraging vector and knowledge graph technologies to map the relationships between data. This allows the system to understand the true intent behind an end-user question, helping to find context-specific answers while differentiating between common synonyms, semantically equivalent words, acronyms, and misspellings.

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This technology needs little training data to be effective, which can alleviate bottlenecks caused by a lack of labeled data and AI expertise. The high costs associated with data labeling make training and fine-tuning LLMs prohibitively expensive for many enterprises. This ease of tuning is another differentiating factor of Kyndi's neuro-symbolic approach. Welsh says many enterprise customers have been burned by slow AI deployments. He describes a large pharmaceutical company that, before working with Kyndi, had been tuning an LLM using six machine learning engineers and data scientists for over six months. Welsh says Kyndi was able to train and tune their model in one day with just the help of a business analyst. In several other cases, Kyndi was able to take AI projects through demo, sandbox validation, and deployment within two weeks.

One Kyndi customer is the U.S. Air Force which uses the company's answer engine system to search its flight mission reports and human intelligence data that spans decades to now. Welsh says the system has been transformational for the military branch, as the self-service Kyndi platform allows intelligence analysts and leaders to ask questions like "Was there an intervention anywhere in the world today?" (meaning a non-conflict interaction with another country's airspace or the like) and get instant answers. Welsh says successful, large-scale use cases like this point to the future of search.

Ryan Welsh, CEO of Kyndi. (Source: Kyndi)

"I think every search bar, and every chat interface, in every enterprise in the world, is going to have an answer engine behind it at some point in the next 10 years. And it's going to be the biggest kind of transition that we've seen in enterprise software," Welsh says, comparing this moment to the transition from on-prem to cloud. "I don't think there's any vendor that is positioned presently to dominate that market."

Welsh predicts the companies that will win during this new era of the enterprise search space are those that had the foresight to have a product on the market now, and though the competition is currently heating up, some of these newer companies are already behind the curve. He estimates they have about 2-3 years and $30 million worth of building to do before they have a product that can be deployed by a military agency or pharma company.

Welsh says Kyndi has found success through its differentiating factors and is checking the boxes for many enterprise customers: "When we come through, and we talk to customers, and they say, 'Hey, we need accurate answers from trusted enterprise content, it has to be explainable, and it has to be easy to run in an enterprise environment, we're going check . . . Check . . . Check."

Fletch Raises $12.5 Million For NLP Engine To Scan Cyber Threat Landscape

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Today, artificial intelligence (AI) cybersecurity vendor Fletch announced it has raised $12.5 million as part of a series A funding round led by venture capital firm True Ventures for a solution to scan the cyber threat landscape.

Fletch's AI-powered solution uses an NLP engine to scan 10,000 articles a day to identify emerging threats, and processes an organization's technology, geographic and industry exposure to better detect cyber threats and provide a risk profile for security teams to refer to in order to identify entry points in their app/tech stack.

The announcement comes as organizations are struggling to keep up with an ever more demanding threat landscape, with cyberattacks increasing by 38% in 2022.

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"No person or team can keep up with all of the cyber threats emerging and evolving every day. With today's technology, you'll still be behind. Getting started with managing cyber threats requires purchasing enterprise technology, long implementation timelines, and large teams to reactively respond to cyber threats," said Grant Wernick, CEO and cofounder at Fletch.

"These systems are designed for enterprise businesses that have the tools and teams in place to react to threats rather than help everyone see potential threats coming and know what to do," Wernick said.

Fletch's answer to this threat landscape is to gather news on emerging threats, as part of an approach the vendor claims enables organizations to identify major threats seven to 10 days before they hit the news cycle.

In fact, Wernick notes that last month Fletch was the first to publicize "ChatGPT Data Leak and Redis Race Condition Vulnerability That Remains Unfixed – #CVE-2023-28858."

The organization's solution falls most comfortably into the artificial intelligence in cybersecurity market, which Grand View Research valued at $13.29 billion in 2021 and anticipate will grow 24.3% annually from 2022 to 2030.

It's competing against a range of established vendors in the market including Darktrace. Darktrace Prevent provides organizations with an AI-driven threat prevention solution that continuously monitors the attack surface, emulates attacks to test for potential vulnerabilities, and automatically identifies and prioritizes risks throughout a network.

Fletch's emphasis on helping organizations to detect threats before they hit the news cycle is its key point of differentiation from other providers in the market.

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