Preparing a text sample to be used for learning a model - 6.5

Natural Language Processing

EnrichVersion
6.5
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
EnrichPlatform
Talend Studio
task
Data Governance > Third-party systems > Natural Language Processing
Data Quality and Preparation > Third-party systems > Natural Language Processing
Design and Development > Third-party systems > Natural Language Processing

This scenario applies only to subscription-based Talend Platform products with Big Data and Talend Data Fabric.

For more technologies supported by Talend, see Talend components.

This Job uses tNLPPreprocessing to divide the input text into tokens. Then, the tokens are converted to the CoNLL format using tNormalize. You will be able to use this CoNLL file to learn a classification model for extracting named entities in text data.

Extracting names entities from text data is a three-phase operation:
  1. Preparing a text sample by dividing it into tokens. The tokens will be used for training a classification model.

  2. Learning a classification model, designing the features and evaluating the model.

    For an example of how to generate a classification model using tNLPModel, see Generating a classification model.

  3. Applying the model on the full text to extract named entities using tNLPPredict.

    For an example of how to extract named entities using a classification model, Extracting named entities using a classification model.

For further information about natural language processing, see Natural Language Processing using Talend Studio.