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Termset Enriching Complex Documents for Microsoft SharePoint from Atidan


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SharePoint is most effective when metadata is applied to your content, dramatically improving search, governance and navigation.  The challenge has always been how to create and maintain consistent, accurate metadata without placing a large burden on business users or your IT department. TermSet is a revolutionary product that uses Machine Learning and Natural Language Processing to enrich SharePoint taxonomies and content by automating accurate consistent metadata. SharePoint Information architecture projects that would traditionally take 12 to 18 months can now be delivered in days.

Natural Language Processing
Hundreds of entities automatically discovered and tagged. Avoids the time and cost of manually creating your information architecture. Full support for all versions of Microsoft Office documents, PDF, MSG and SharePoint lists and library content (including attachments).

Sentiment and language Analysis
Detailed and accurate scoring of the positive and negative sentiments for SharePoint content. Understand your customers better to gain a competitive edge.
Concise summaries created for documents. Increase productivity by giving an instant overview of the information contained within large documents.
Taxonomy Management
Creates and maintains SharePoint taxonomies unique to your content and utilises existing taxonomies for tagging. Ensures accurate & consistent metadata across your organisation. Full support for synonyms and multi-lingual taxonomies.
Pattern Matching
Advanced methods for recognising reference numbers and any other patterns. Saves time and reduces errors and removes the need for manual tagging of content.
Language Support
We can apply metadata in English, German, French, Spanish, Swedish, Russian, Italian and Portuguese. Able to recognise over 100 languages. Reduce time and cost for multi-lingual IT consultancy.
Context Sensitive Tagging
No requirement for rules based tagging as the product can assess the correct context of words and apply accurate and consistent metadata. Reduces the risk of incorrect classification and the burden of managing large rule sets.

Published in: Software
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