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• Tags: in this tab the user will define all of the semantic tags to be used and also will define what recognizers and settings each tag will use.
  
  

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• Pipelines: here the user can add new, delete o update pipelines. Pipelines defines what stages are included in the pipeline before recognizer stages are added to the pipeline. So for example you could have stages like white space tokenizer, text case analyzer or a stop words identifier.

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• Datasets: Here the user can view the datasets loaded into the application to perform test runs and/or training of machine learning models. In this screen the user can define what fields to process from the dataset file and the way to split the text to feed the pipeline.  The user cannot upload datasets at the moment, so datasets need to be placed in a special folder in Saga Server file system.

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• Background Processes: In this screen the user can monitor background processes running. For example, when running a test run against a dataset, this process could take a long time to complete, so the user can view the progress in this screen. 

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• Search Interface: In this screen the user can review results from a Test Run.  So the flow will be something like: 
  1. The user adds a semantic tag 
  2. Then adds recognizers to this tag and configure them.
  3. The user tests effectiveness of the Tag and its recognizers by running a test run against a dataset file.
  4. To review results will open the search interface to check how well (or bad) text was tagged

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