The expanding ease and utility of text analytics and natural language processing

The strategic gains of text analytics are myriad and, quite possibly, greater today than they’ve ever been before. The influx of advanced machine learning approaches impacting natural language technologies makes textual analysis more accessible to the enterprise than it was even 5 years ago.

Statistical model techniques also produce the benefit of accelerating tradi- tional natural language processing (NLP) methods to reduce their time-to-value. Conversely, pairing these conventional methods with their newer statistical counterparts heightens the accuracy of text analytics, which, in turn, increases the use cases for the full spectrum of natural language technologies.

Time-honored applications of sentiment analysis and contractual reviews are as prevalent as they ever were. There’s also an array of more modern deployments, including the automation of regulatory reports, spoken interfaces with front- and back-end IT systems, and generative text summaries of documents and visualizations.

Consequently, natural language technologies—including natural language understanding (NLU), natural language generation (NLG), and conversational AI—are embedded in everything from business intelligence (BI) solutions to the now ubiquitous remote conferencing options.

The expanding number of choices in this space means there’s also a burgeoning assortment of technological approaches to account for when selecting the right one for the enterprise. According to Franz CEO Jans Aasman, “Any technology having to do with text and unstructured text, that’s text analytics. Parsing a text is text analytics. But, doing entity extraction or computing a word embedding is text analytics, too.”

Understanding the implications of these different methods is critical for obtaining the accuracy, traceability, explainability, and time-to-insight needed by organizations to achieve any desired objective from text analytics.

Read the full article at KMWorld.