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Sinequa Upgrades Natural Language Processing and Data Connectors to Boost Performance of Information Workers in the Digital Workplace

Sinequa announced several new capabilities in its platform, now generally available. These critical capabilities include new connectors to expand the range of systems and formats that can be ingested. Additionally, there is support for named entity recognition packaged within the platform, for example, weights and measures and personally identifiable information (PII) across different geographies. Most notably, there are new capabilities to extend the platform's ability to automatically conduct intelligent classification across vast amounts of indexed enterprise content and data.

"With the latest release, Sinequa can better serve the needs of customers in various industries like life sciences, manufacturing, and finance, which requires recognizing concepts and highly technical and specific vocabularies," said Philippe Motet, vice president of engineering at Sinequa. "We are improving the efficiency and speed at which employees can find relevant information and insights needed to make smarter and more effective business decisions."

Extended NLP Capabilities and Out-of-the-Box Personal Data Identification

Sinequa has also expanded its natural language processing (NLP) capabilities with additional support for named entity recognition.

For example, the new release recognizes PII, which enables instant identification of 24 categories of PII, enabling organizations to comply with privacy-protection legislation like GDPR more effectively. These entities cover 89 types within these categories. Some examples include BBAN, IBAN, credit card number, date of birth, driver's license number for 13 countries, license plates for 11 countries, passport number for 13 countries, various national identification numbers for 14 countries, etc.

The Power of AI without the Complexity

Building on Sinequa's support for production-scale machine learning, the latest release provides several new, interrelated capabilities to help organizations attain the powerful benefits of AI-powered intelligent search quickly and without the need for rare data science skills or machine learning expertise. In fact, all tasks concerned with model building and validating, and keeping accurate predictions over time by adjusting to concept and data drift, are now supported within the platform.

This release introduces an Intelligent Labeling Application that is dedicated to the data management of the classification algorithm. A critical design goal of this new application is to provide a user experience that compels subject matter experts to use the application and thereby provide their domain expertise. The purpose of the application is two-fold. It enables organizations to bootstrap an objective training set without relying on data scientists, and it also provides a means to leverage the feedback of subject matter experts to improve model predictions over time, while handling concept drift and data drift by automatically requesting their validation for ambiguous predictions.

Broader Connectivity to On-Premise Enterprise Applications and Cloud Environments

Sinequa continues to broaden its support of increasing volumes and diversity of enterprise data retained in cloud and on-premise enterprise applications with a constantly growing family of 200+ smart connectors. The latest version of the Sinequa platform adds, for example, connectors to the IBM Filenet P8 V5 application as well as for the Perforce Helix core application used for software version control.

Published Thursday, May 07, 2020 7:17 AM by David Marshall
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