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1. Introduction

The purpose of SAGA UI is to help on the creation of the model (tags, patterns, recognizers recognizer settings) that eventually SAGA Library will use to process text.

The UI is aimed to SME's SMEs (subject matter experts), basically business people that are not necessary tech savvy but know a lot about the business need and the things that are needed to be identified within the text to the processed.

The UI permits the collaboration between SME's SMEs and tech people, all using the application at the same time.  So creation and testing of the model which is a difficult and time consuming effort task can be accelerated to produce results sooner.

As will be described in following sections, the functionality of the UI is organized by semantic tags. So first of all, the user thinks on the things that he/she wants to identify in the text (the 'what'), then thinks on 'how' to identify those tags using the recognizers provided in the application. 

2. Exploring the UI

The UI is pretty simple, it has a main tab selector at the top where you can select which one of the 4 areas you want to work on:

• 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.