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Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.
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Communications Mining user guide

Last updated Aug 1, 2025

Training using Teach Label (Explore)

Note: You must have assigned the Source - Read and Dataset - Review permissions as an Automation Cloud user, or the View sources and Review and annotate permissions as a legacy user.

Teach is the second step in the Explore phase and its purpose is to show predictions for a label where the model is most confused if it applies or not. Like previous steps, we need to confirm if the prediction is correct or incorrect, and by doing so provide the model strong training signals. It is the most important label-specific training mode.

Note:

Teach Label is a training mode designed exclusively for annotating unreviewed messages. As such, the reviewed filter is disabled in this mode.

Key steps

  1. Select Teach Label from the dropdown menu as shown in the following image.
  2. Select the label you wish to train, where the default selection in Teach mode is to show unreviewed messages.
  3. You will be presented with a selection of messages where the model is most confused as to whether the selected label applied or not. This means you should review the predictions and apply the label if they are correct, or apply other labels if they are incorrect.
    Note:
    • Predictions will range outwards from ~50% for data with no sentiment and 66% for data with sentiment enabled.
    • Make sure you apply all other labels that apply as well as the specific label you are focusing on.



You should use this training mode as required to boost the number of training examples for each label to above 25, so that the platform can then accurately estimate the performance of the label.

The number of examples required for each label to perform well will depend on a number of factors. In the Refine phase we cover how to understand and improve the performance of each label.

The platform will regularly recommend using Teach Label as a means of improving the performance of specific labels by providing more varied training examples that it can use to identify other instances in your dataset where the label should apply.

Solutions for insufficient Teach examples

You may find after Discover and Shuffle that some labels still have very few examples, and where Teach Label mode does not surface useful training examples. In this case, you are recommended to use the following training modes to provide the platform with more examples to learn from:



Option 1 - Search

Searching for terms or phrases in Explore works the same as searching in Discover. One of two key differences is that in Explore you must review and annotate search results individually, rather than in bulk. You can search in Explore by simply typing in your search term in the search box at the top left of the page.



However, too much Search can biasyour model which is something we want to avoid. Add no more than 10 examples per label in this training mode to avoid annotating bias. Make sure you also allow the platform time to retrain before going back to Teach mode.

Option 2 - Label

Although training using Label is not one of the main steps outlined in the Explore phase, it can still be useful in this phase of training. In Label mode, the platform shows you messages where that label is predicted in descending order of confidence, that is, with the most confident predictions first and least confident at the bottom.



However, it is only useful to review predictions that are not high-confidence, above 90%. This is because when the model is very confident, that is, above 90%, then by confirming the prediction you are not telling the model any new information, it is already confident that the label applies. Look for less confident examples further down the page if needed. Although, if predictions have high confidences and are wrong, then make sure to apply the correct labels, thus rejecting the incorrect predictions.

Useful tips

  • If for a label there are multiple different ways of saying the same thing, for example, A, B, or C, make sure that you give the platform training examples for each way of saying it. If you give it 30 examples of A, and only a few of B and C, the model will struggle to pick up future examples of B or C for that label.
  • Adding a new label to a mature taxonomy may mean it has not been applied to previously reviewed messages. This then requires going back and teaching the model on new labels, using the Missed label function.

  • Key steps
  • Solutions for insufficient Teach examples
  • Useful tips

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