An overview of the Text Analysis toolset

The Text Analysis toolset contains tools that perform natural language processing on text. Text can be classified or transformed, and entities such as addresses can be extracted.

Tools in the Text Analysis toolset

Tool	Description
Classify Text Using Deep Learning	Runs a trained text classification model on a text field in a feature class or table and updates each record with an assigned class or category label with each class having a confidence value.
Extract Entities Using Deep Learning	Runs a trained named entity recognizer model on text files in a folder, or a text field in a feature class or table, to extract entities and locations (such as addresses, place or person names, dates, and monetary values) in a table. If the extracted entities contain an address, the tool geocodes the addresses using the specified locator and produces a feature class as an output.
Process Text Using AI Model	Processes text from various types of sources, such as text fields in feature classes or tables, or text files in a folder, to support a variety of use cases including text transformation, entity recognition, text classification, text generation, translation, summarization, and so on. The tool uses custom third-party models or deep learning models trained using the Train Text Classification Model, Train Text Transformation Model, and Train Entity Recognition Model tools.
Train Entity Recognition Model	Trains a named entity recognition model to extract a predefined set of entities from raw text.
Train Text Classification Model	Trains a single or multilabel text classification model to assign a predefined category or label to unstructured text.
Train Text Transformation Model	Trains a text transformation model to transform, translate, or summarize text.
Transform Text Using Deep Learning	Runs a trained sequence-to-sequence model on a text field in a feature class or table and updates it with a new field containing the converted, transformed, or translated text.