Preparing a text sample to be used for learning a model - 7.0

Natural Language Processing

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
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
EnrichPlatform
Talend Studio

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.

tHDFSConfiguration is used in this scenario by Spark to connect to the HDFS system where the jar files dependent on the Job are transferred.

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode (Yarn client or Yarn cluster):
    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.