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Using Natural Language Processing to Uncover Valuable Insights in Text-based Data

In this special guest feature, Ryan Welsh, Co-founder and CEO of Kyndi, discusses how organizations are leveraging the latest natural language processing techniques to enable sophisticated natural language understanding. Ryan started Kyndi in 2014 with a vision of creating a world where AI would empower humans to do their most meaningful work. Under his leadership, Kyndi has created the natural language enablement category, offering a powerful Natural Language Enablement Platform and natural language-enabled solutions. Ryan received his B.A. in Anthropology from The Catholic University of America, his M.S. in Applied Math/Economics from Rutgers University, and an M.B.A. from the University of Notre Dame.

According to Deloitte, as much as 80% of all information is hidden in unstructured, text-based data living in various systems inside and outside of the companies. Many organizations struggle to extract relevant insights when they search for answers in text data, mainly because the search tools they are using are not designed to effectively and efficiently process unstructured data. 

Remote and hybrid work has exacerbated the pain of unsatisfying search outcomes because so many employees work from their own locations and access information at different hours, making information sharing within an organization a major challenge. You can’t simply reach out to your colleague sitting next to you for answers whenever you think necessary. Instead, employees habitually turn to keyword-search tools to find relevant information.   

Using these inadequate tools, employees spend more time (up to 3.6 hours per day/938 hours per year) looking for information now than they did pre-pandemic, particularly with text-based data such as documents and messaging. Conventional search tools are not equipped to solve this problem because they are built to search for keywords in structured data, a technique that is not designed to find the correct answers in text-based data effectively. 

So how does an enterprise fix this poor search experience and get the right answers to its employees and customers quickly whenever they are searching for information hidden in unstructured text data? How does it change its search approach to make it more than keyword-oriented, so everyone can ask questions using their own phrases? After all, without access to all the important business information that has been collected and stored in different formats, the business’s long-term success may eventually be at risk.

Keyword search has many limitations

With any traditional search engine, you’re typing keywords, and the engine returns results only if they contain the exact words. When used to search unstructured text data, users must read multiple results thoroughly in order to find the answer to the question because they are not being pointed directly to the sentences containing the answer. These keyword search engines perform token matching when what is needed is a search engine that understands the contextual meaning of language. 

This is where Natural Language Search comes into play. If your company hasn’t enabled natural-language processing (NLP) yet to search your content, enable self-service customer experiences, or accelerate market insights discovery, you’re behind the curve. NLP is a clear trend in 2022 and beyond. It is a promising technology that can drive game-changing outcomes for text-based searches because it uses sophisticated AI techniques rather than just simple keyword matching.

Natural language search: The key to delivering  better answers

Natural language search is an advanced search approach that employs artificial intelligence techniques to accurately interpret a question expressed in full natural language and bring back the most relevant answers based on that deep understanding of both the question and the underlying data. With natural language search, you can ask a question using your own words, as if you were asking another person, and expect highly accurate and contextual answers in return–no more fruitless searches.  

In contrast, with traditional keyword search, users have to choose specific words and syntax to ask their questions, and the search engine retrieves documents or data that may or may not be helpful to the user. 

Natural-language search requires Natural Language Understanding (NLU), a branch of artificial intelligence that teaches computers to understand language. NLU is a major component of human-computer interaction, a multi-disciplinary field focused on the interaction of humans and computers. Language understanding makes for a more user-friendly search experience. Currently, the main application of NLU is to create chat- and voice-enabled bots that can interact with the public without human assistance. 

Many companies, such as Amazon, Apple, Google, and Microsoft – and startups – have NLU projects under way. But NLU is being used increasingly in natural-language search use cases, proving to be a valuable tool to empower more business users to quickly and easily find meaningful insights when they search for answers in text data.  

In addition to NLU, a Natural Language Search solution should also provide critical capabilities, such as search analytics, easy and fast tuning and optimization, model benchmarking and monitoring, and building a continuous data pipeline. Enterprise data security measures should also be part of the process.  

Language has three main properties: syntax, semantics, and pragmatics. It has syntactic structure, semantics supply the ostensible meaning, and pragmatics is how context contributes to meaning. Sophisticated machine learning and AI systems better understand all three properties of language. In keyword search engines, all this is greatly lacking.

How can businesses use NLP to drive impact?

Popular use cases for Natural Language Search include implementing self-service search for both customers and support agents to optimize customer experience while improving support efficiency, and accelerating policy and procedure search to meet compliance requirements and minimize risks. Another is gaining accurate and deeper market insights for competitive research (for market intelligence teams and researchers) to develop winning business strategies.

Only when a company transforms itself into a Natural Language-enabled Enterprise can it set itself on a better and more promising course to growing its business.

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