How to use machine learning for content annotation

Unleash the power of the latest deep learning models
Solve your NLP tasks with state of the art Transformer models like BERT with CAM

When to use

When the rules required to properly execute the task are not known or to complex to formulate. You will also need an annotated set of data with at least a hundred examples for each category.

What can you do with machine learning models

Classify sentences or documents, identify named entities or concepts, detect sentiment and others.

How

Create and train your own model.  Alternatively, use the transfer learning approach – take an existing complex like a Transformer model and retrain it for your task.
Use Jupyter notebook to define and train Python ML models.
Here is an example of notebook for a BERT based Tensorflow model.
Train deep learning models on environments like Google Colab. Remember, training will work best on a GPU fitted server.
Once your model is ready, use the CAM AdminUI to import it and set up the serving environment.

Supported models

Gate plugins

• Naive Bayes, Maximum Entropy, and Decision Trees (MALLET)
• linear chain Conditional Random Fields (MALLET)
• Support Vector Machines (LibSVM)

Python models including neuronal deep learning models

• scikit-learn
• spaCy
• Tensorflow
• PyTorch
• Transformer models: HuggingFace

Enjoy the machine learning in an industrial grade production setup