The expanding ease and utility of text analytics and natural language processing
Digital agents
Virtual agents or bots are increasingly being employed in text analytics use cases. In some instances, their appeal is the sheer scale they enable for a particular application. Kohli explored an application in which bots are deployed at scale for “looking at documents all over the web, extracting data that can be used for anti-money laundering.” In this and other IDP use cases, the digital agents are extracting the underlying semantics behind the entities or objects to fuel their analysis. In this specific use case, this includes looking “for records of that entity who owns the account, how they interact with other accounts as well, what patterns they have, and then [spotting] these money flows,” Kohli said. Bots are also employed to interact with customers as a means of rapidly informing a company's representatives of their concerns or reasons for calling.
“In the financial services space, a customer can engage with a bot asking for specific financial advice or a specific situation or financial state,” Segovia commented. “We can summarize entire conversations with a financial advice type of bot, send it over to the financial advisor, and then the advisor has enough context to engage in a call directly with the client.” This application is particularly interesting because it requires NLU to comprehend customers’ concerns, may involve conversational AI for the bot's conversation with the customer, and necessitates NLG for the summaries. It also involves speech recognition and perhaps even spoken-word interfaces for systems via natural language. “Text and speech is the currency of thought,” Segovia reflected. “If we use natural language to interface with machines, text to speech, that ecosystem is extremely robust. Natural language processing with transformer architecture in particular enables a lot of that.”
Question-answering
Quite possibly, there are as many applications of text analytics as there are ways of facilitating natural language technologies. Those use cases—and their enterprise worth—are only enlarging as the unstructured data divide continues to broaden across industries. Natural language interactions between humans and IT systems are swiftly becoming normative. However, the greater utility of text analytics, whether or not that's preceded by
speech-to-text conversions, is the capacity to analyze the reams of unstructured text in the form of documents, conversational transcriptions, social media streams, and more.
The ability to make this information searchable, in natural language, and amenable to question-answering for what Aasman termed “business insights” is, for many organizations, the upper echelon of text analytics. “It's ultimately about the business questions you can ask of the text, not whether you can find words in the text,” Aasman specified.
Department or enterprise-wide knowledge only aids in this endeavor—especially when it becomes a matter of legal or regulatory interest.