No longer is the issue content, we have more content than we know what to do with. The issue is understanding and knowing the contextual relevance of all of that content. And, how to bring relevance to all of that unstructured data, automatically. Which documents are Relevant in context of your Now Subject Specific Informational Need Moment. Consistently Accurate, In Context, Objectively and Automatically.
Applying a Bayesian and Heuristic approach used to be good enough for making general assumptions of category and loose subject relevance of unstructured data. Today, we demand instant, accurate, contextual, objective and relevant results for the information we seek from the Yottabytes of content.
In past, Text Analytic implementations used statistical inference / probability approaches (Bayesian / Heuristics) where lists of keywords and key terms were compiled per subject matter and then referenced to iteratively try and determine what a particular document / data set was about. Those best-efforts results then used to categorize the target content. Similar to how some Document / Content Management Systems work for adding meta data (tags). Usually though, Content and Document Management Systems will require Human Intelligence to first determine how a document being added to a management system should be categorized for a more accurate retrieval purpose.
Let’s hope that Human Intelligence component isn’t having a bad day or the best efforts of objectivity goes out the window.
Statistical inference and probability approaches were okay, yesterday.
Today, we demand instant, accurate, contextual, and objectively relevant results. This requirement would be absolutely daunting if Artificial Intelligence and Machine Learning technologies weren’t available! In fact, the xAIgent (pr: ex-agent) RESTful web service employs patented AI and Machine Learning technology to Accurately (83%), Contextually (per target text), 100 % Objectively (Artificial Intelligence not Human) and Automatically provide key phrase and keyword results for Doc-Tags. The only Automatic, Objective, Contextually Accurate Document Tagging solution.
That’s correct. Doc-Tags will take a Word (.docx) file or text file and automatically with contextual accuracy, add keyword and key phrase meta data Tags to the target file’s Meta Data Tag property.
Doc-Tags is Free to Use for your first three most accurate document tags. Want more insight for your document meta data, increase the number of tags for a small subscription fee, which will provide up to 30 Contextually Accurate document tags per target content.
Try Doc-Tags for Free Today! http://www.Doc-Tags.com
Doc-Tags and xAIgent are also great tools for:
Law Enforcement, Legal Research
Publishing – Front of Book (Table of Contents / Abstracts / ) | Back of Book- Indexing
Scientific Research Publishing – Abstract keyword / key phrase highlighting
“I’m from Missouri” – here’s where we Show You the technical aspects of the only Automatic, Contextually Accurate Keyword and Key Phrase Extraction Service available. Not even IBM or Microsoft have this. https://xAIgent.com