Beyond NLP: 8 challenges to building a chatbot

“Our range of analytical tools—including predictive modeling, data mining, scorecards, and machine learning—help publishers to automate their existing processes, resulting in increased efficiencies at a lower cost,” says Vidur Bhogilal, vice chairman of Lumina Datamatics. For India’s digital solutions vendors, the application of AI and NLP has been ongoing for years, primarily to accelerate internal production processes and meet the publishing industry’s demands for faster, cheaper, and shorter turnaround time. From sifting through the large volume of incoming content to flagging content and process anomalies, AI/NLP has been indispensable. As for Newgen KnowledgeWorks, the opportunity for authors and publishers to self-serve (by using various tools on offer) has seen its focus shifting increasingly toward supporting users, deploying tools, and training teams around the world on its solutions.

Amazing Examples Of Natural Language Processing (NLP) In Practice

“Increasingly, we will be creating our own content to support the impressive technologies—based on AL, ML, NLP, Big Data, or analytics, for instance—that we have developed,” company president Maran Elancheran says. With AI, the reference segment of a STM book or journal can be handled effectively using an online or in-house reference library. “AI can apply and highlight the keywords such as author name, product name, subject, and bibliographic for index process by referring to general master rules and the keyword library. The past year has seen MPS working on solutions featuring chatbots for clients in the areas of onboarding, employee self-service, performance support, customer support, and game-based learning. Variable tracking, analytics, and multimedia displays are among the rich array of features developed for these solutions.

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When you ask Siri for directions or to send a text, natural language processing enables that functionality. In essence, the NLP does not address any of the challenges that you typically face in developing a real-world line of business application. It simply presents the opportunity to deliver a broader and more satisfying experience using a chat interface. Faris Sweis is senior vice president and general manager of the developer tools business at Progress.

Natural language processing is the key to communicating with users, but doesn’t solve the business problem on its own

Every day, humans exchange countless words with other humans to get all kinds of things accomplished. But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other. That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence.

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• Natural language processing is behind the scenes for several things you may take for granted every day.
• That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence.
• In essence, the NLP does not address any of the challenges that you typically face in developing a real-world line of business application.
• If such an evolution is not taken, chatbots will continue to be costlier to develop and maintain than traditional applications.

Much has been said about cognitive technologies, which include artificial intelligence (AL), machine learning (ML), natural language processing (NLP), robotic process automation, and rule-based or expert systems. Much fear—of robot overlords taking over the world (and our brains)—has also been raised at the same time. Then there is Kashyap Vyas’s “7 Ways AI Will Help Humanity, Not Harm It” (Interesting Engineering, December 3, 2018) providing a completely different line of thought.

• Here we highlight some of the everyday uses of natural language processing and five amazing examples of how natural language processing is transforming businesses.
• “Online book recommendation in many of the retailer sites currently uses some form of AI that makes guesses based on past purchases and browsing,” says Uday Majithia, assistant v-p of technology, services, and presales at Impelsys.
• “Our range of analytical tools—including predictive modeling, data mining, scorecards, and machine learning—help publishers to automate their existing processes, resulting in increased efficiencies at a lower cost,” says Vidur Bhogilal, vice chairman of Lumina Datamatics.
• As for Newgen KnowledgeWorks, the opportunity for authors and publishers to self-serve (by using various tools on offer) has seen its focus shifting increasingly toward supporting users, deploying tools, and training teams around the world on its solutions.
• “Increasingly, we will be creating our own content to support the impressive technologies—based on AL, ML, NLP, Big Data, or analytics, for instance—that we have developed,” company president Maran Elancheran says.

One of the most challenging and revolutionary things artificial intelligence (AI) can do is speak, write, listen, and understand human language. Natural language processing (NLP) is a form of AI that extracts meaning from human language to make decisions based on the information. This technology is still evolving, but there are already many incredible ways natural language processing is used today. Here we highlight some of the everyday uses of natural language processing and five amazing examples of how natural language processing is transforming businesses. Over at Lumina Datamatics, Big Data and predictive analytics have enabled clients to manage large volumes of structured and unstructured content.

New Tech Forum provides a venue to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Many of the new chatbot vendors are trying to solve these challenges by introducing a richer declarative syntax that enables developers to define the goals of the bot and handle much of the heavy lifting related to system integration, conversation flow, and persistence management within the chatbot framework. If such an evolution is not taken, chatbots will continue to be costlier to develop and maintain than traditional applications. Natural language processing is behind the scenes for several things you may take for granted every day.

Bhogilal and his team also use AI, NLP, and ML technologies to automate Lumina Datamatics’ copyediting processes. Then there is cognitive analysis and Smart Test technology for analyzing the quality and suitability of resources. Publishers can leverage AI in analytics and marketing to find the right audience for a title, or to help readers discover what they need. “Online book recommendation in many of the retailer sites currently uses some form of AI that makes guesses based on past purchases and browsing,” says Uday Majithia, assistant v-p of technology, services, and presales at Impelsys. But through AI — specifically natural language processing (NLP) — we are providing machines with language capabilities, opening up a new realm of possibilities for how we’ll work with them.