Preparing a text sample to be used for learning a model - 7.2

Natural Language Processing

Version
7.2
Language
English (United States)
Product
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
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.

    You can find an example of how to generate a named entity recognition model on Talend Help Center (https://help.talend.com).

  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.

    You can find an example of how to extract named entities using a classification model on Talend Help Center (https://help.talend.com).

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

You can find more information about natural language processing on Talend Help Center (https://help.talend.com).

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 Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • When using on-premise 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.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).