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Making Content Management Better

The Intelligence of Document Tagging
Our experiences in AI shows that machines are not going to make us redundant, but that they will augment the human workforce. AI as a technology has only solved half of the equation. AI technology has had to be contextualized in the domain of an industry, information that only humans have possessed. Therefore, people who can apply their knowledge and experience in meaningful ways with AI will be increasingly important.

Human beings possess the ability to process experience, knowledge, intuition and common sense for appropriate critical outcomes. When it comes to surfacing accurate, contextualized highlight information - meta tags, synopsis, annotations, labels... human beings are not the only resource and now may not even be the right resource. After all, as human beings we are all influenced by our own experiences and surroundings which infuses bias and how we understand information. To provide high quality highlight details - key phrases, keywords, text labels, text annotations, content tags and the like - for these data tags to be truly useful, they must be objective and unbiased.

Being able to process Yotabytes of content with pure objectivity (zero bias) is where AI and Machine Learning come to the fore. Of course it's possible to amass hundreds, even thousands of Text Labeling / Analytics specialists, however, the way one person understands a document is quite often different from how another person interprets that same textual content. Compound that bias with the volumes of content does make for an extremely daunting task, especially if the content has already been published without an authors contextual key phrase tags. This is where an AI and Machine Learning infused technology such as Doc-Tags, powered by the patent-backed xAIgent API, enhances our human processes. Take this type of AI + ML technology and give it the capability to parse Text Labels, Key Phrases, Text Annotations, Tags, from any text-based content, of any subject matter with results having contextual relevance and high accuracy gives us a whole new world of objective understanding at our finger tips.

Doc-Tags is a free to use Windows application engineered to process Text files and Word Documents, out of the 'box', exposing each document's key phrases (Tags) with contextually relevant accuracy. The Key Phrase results (Tags) of each document's processing are placed automatically into the documents profile Tag Property giving the Doc-Tags' processed document a high degree of relevance with contextual accuracy - the ultimate goal of any Content / Document Management System.

Not only will Doc-Tags give any textual content a high-level and immediate, accurate, contextual relevance, Doc-Tags may be set to process individual files or directories of files with each document processed logged in its database along with the Key Phrase Tag results for quick referential services. Which documents contain these keywords / key phrases used in this manner? Search and Compare files and directories of files for keywords and key phrases (Tags) using Doc-Tags' built-in reporting. Or, customize your own reports using the Doc-Tags XML Database.

A whole new world of document insight is at your finger tips...
Doc-Tags Automatic Document Description Tagging - by DBI Technologies Inc.
Today is the last day you read / re-read a document to define it for an Enterprise Content Management System or to publish content on the Semantic Web

Feature Highlights:


• Automatically Create Contextually Accurate Tags for a Document


• Automatically Create Contextually Accurate Tags Document Folder


• Automatically Add Document Meta Data Tags


• Subject Domain Agnostic - additional training NOT required


• Automatic Processing - supervision is NOT required


• Document Tags are presented by Weighted Importance


• Process Reporting ...


Tags Generated


Documents Processed


Document Location


• Examine Comparative Document Tags


• Find Documents with 'These' Tags


• Multi Language Support ...









• Korean


• Spanish


• Contextual Key Terms Automatically Extracted for Each Document

Turning content into valuable resources
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