Text

The Text tag shows text that can be labeled. The text template is divided into the following segments:

• Information extraction: Includes NER and extracting relations among entities.
• Classification: Includes text classification and multi-class classification.
• Text summarization.

Named Entity Recognition

Named Entity Recognition (NER) refers to the identification and classification of entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

The Generative AI Lab offers support for two types of labels:

• Simple labels for NER or assertion models;
• Binary relations for relation extraction models.

Assertion Labels

The syntax for defining an Assertion Status label is the same as for the NER labels, with an additional attribute - assertion which should be set to true (see example below). This convention is defined by Generative AI Lab users which we exploited for identifying the labels to include in the training and prediction of Assertion Models. A simple Labeling Config with Assertion Status defined should look like the following:

<View>
<Labels name="ner" toName="text">
	<Label value="Medicine" background="orange" hotkey="_"/>
	<Label value="Condition" background="orange" hotkey="_"/>
	<Label value="Procedure" background="green" hotkey="8"/>
	<Label value="Absent" assertion="true" background="red" hotkey="Z"/>
	<Label value="Past" assertion="true" background="red" hotkey="X"/>
</Labels>
<View style="height: 250px; overflow: auto;">
	<Text name="text" value="$text"/>
</View>
</View>

NOTE: Notice assertion=”true” in Absent and Past labels, which marks each of those labels as Assertion Status Labels.

Classification

The choices tag is used as part of the classification projects to create a group of choices. It can be used for a single or multiple-class classification. According to the parameters used along with the choices tag, annotators can select single or multiple choices.

Parameters

The Choices tag supports the following parameters/attributes:

<!--text classification labeling config-->
<View>
  <Text name="text" value="$text"/>
  <Choices name="surprise" toName="text" choice="single" required='true' requiredMessage='Please select choice'>
    <Choice value="surprise"/>
    <Choice value="sadness"/>
    <Choice value="fear"/>
    <Choice value="joy"/>
  </Choices>
</View>

When using the perRegion attribute, choices can be defined for each chunk annotation as shown below:

NER with Relation Extraction

Generative AI Lab also offers support for relation extraction. Relations are introduced by simply specifying their label in the project configuration.

<Relations>
    <Relation value="CancerSize" />
    <Relation value="CancerLocation"/>
    <Relation value="MetastasisLocation"/>
  </Relations>

Information Extraction

For simple NER related tasks, the configuration just needs the Text and Labels tags. The Labels tag provides a set of labels for labeling regions in your tasks. Use the Labels tag to create a set of labels that can be assigned to identified region and specify the values of labels to assign to regions. For example, you have the following JSON to label, as shown below.

{
    "text": "The patient is a pleasant 17-year-old gentleman who was playing basketball today in gym. Two hours prior to presentation, he started to fall and someone stepped on his ankle and kind of twisted his right ankle and he cannot bear weight on it now. It hurts to move or bear weight. No other injuries noted. He does not think he has had injuries to his ankle in the past. He was given adderall and accutane.",
    "title": "MyTestTitle"
}

The configuration in this case looks as shown below.

The model parameter in the Label tag must be specified whenever a pre-trained model is used in the project for pre-annotation purposes. It is automatically defined when a model is chosen from Reuse Resources.

For the case of relation extraction, the configuration additionally needs a Relations tag apart from Text and Labels. For the same input, the configuration would look as shown below.

Classification

For a classification task, the configuration needs the Text and Choices tags. For instance, you have the input JSON as shown below. The Choices tag is used to create a group of choices (a set of Choice tags), with radio buttons or checkboxes. It can be used for single or multi-class classification.

{
    "text": "The patient is a pleasant 17-year-old gentleman who was playing basketball today in gym. Two hours prior to presentation, he started to fall and someone stepped on his ankle and kind of twisted his right ankle and he cannot bear weight on it now. It hurts to move or bear weight. No other injuries noted. He does not think he has had injuries to his ankle in the past. He was given adderall and accutane.",
    "title": "MyTestTitle"
}

For simple single-class classification, the configuration is shown below.

In the case of multi-class classification, the configuration would look as shown below.

Text summarization

For a simple text summarization, the configuration just needs the Text and TextArea tags. The TextArea tag is used to display a text area for the user input. It is mainly used for transcription, paraphrasing, or captioning task.