Category Archives: Deep Semantic Analytics

Deep Semantic Analytics

Recorded webinar: Why You Need Deep Semantic Analytics

Last July 13th we delivered our webinar “Why You Need Deep Semantic Analytics”, where we explained how to achieve a deep, automatic understanding of complex documents. Thank you all for your interest.

During the session we covered these items:

  • Automatic understanding of unstructured documents.
  • What is Deep Semantic Analytics? Comparison with conventional text analytics.
  • Where it can be applied.
  • Case study: due diligence process.
  • Ideal features of a Deep Semantic Analytics solution.
  • MeaningCloud Roadmap in Deep Semantic Analytics.

IMPORTANT: you can find a more literary explanation of some of the items we covered, including the due diligence practical case, in this article.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)
Continue reading


Deep Semantic Analytics: A Case Study

Scenarios that can benefit from unstructured content analysis are becoming more and more frequent: from industry or company news to processing contracts or medical records. However, as we know, this content does not lend itself to automatic analysis.

Text analytics has come to meet this need, providing powerful tools that allow us to discover topics, mentions, polarity, etc. in free-form text. This ability has made it possible to achieve an initial level of automatic understanding and analysis of unstructured documents, which has empowered a generation of context-sensitive semantic applications in areas such as Voice of the Customer analysis or knowledge management.

Continue reading


Why you need Deep Semantic Analytics (webinar)

Achieve a deep, automated understanding of complex documents

Conventional Text Analytics enable a first level of automatic understanding of unstructured content, achieved through its ability to extract mentions of entities and concepts, assign general categories or identify the polarity of opinions and facts that appear in the text. However, these isolated information elements do not reflect the wealth of information provided by these documents and impose limitations when it comes to finding, relating or analyzing them automatically.

Deep Semantic Analytics represents a step beyond conventional text analytics by providing features such as snippet-level granular categorization, detection of complex patterns, and extraction of semantic relationships between information elements in the document.

Continue reading