tNLPModel properties for Apache Spark Batch - 6.5

Natural Language Processing

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These properties are used to configure tNLPModel running in the Spark Batch Job framework.

The Spark Batch tNLPModel component belongs to the Natural Language Processing family.

The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric.

Basic settings

Define a storage configuration component

Select the configuration component to be used to provide the configuration information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local system.

The configuration component to be used must be present in the same Job. For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write the result in a given HDFS system.

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to Repository. When you create a Spark Job, avoid the reserved word line when naming the fields.

Click Sync columns to retrieve the schema from the previous component connected in the Job.

Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this option to view the schema only.

  • Change to built-in property: choose this option to change the schema to Built-in for local changes.

  • Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the [Repository Content] window.

The first column in the input schema must be token and the last column must be label.

You can insert columns for features in between.


Built-In: You create and store the schema locally for this component only.


Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Feature template

Features: Select from the list the token-level features to be generated.

  • POS tag: Part-of-speech tags are labels that are assigned to words according to their role in a sentence, for example, verb, noun or adjective.

  • NER tag: Named Entity Recognition tags are labels assigned to tokens which are the names of things. For example, "PER" for a person name.

  • token : Original word segment.

  • lemma: Produces the lemma form for the word, for example, "take" for "takes", "took" or "taken"

  • stem: Produces the root form of the word, for example, "fish" for "fishing, "fished" or "fishes".

  • lowertoken: Produces the original token in lowercase

  • tokenisnumeric: The token is a number.

  • tokenispunct: The token is a punctuation mark or multiple punctuation marks.

  • tokeninwordnet: The token can be found in WordNet.

  • tokeninstopwordlist The token is a stop word, for example "the", "and", "then" or "where".

  • tokeninfirstnamelist: The token appears in the list of first names.

  • tokeninlastnamelist: The token appears in the list of last names.

  • tokensuffixprefix: The token prefix or suffix.

  • tokenismostfrequent: The token is among the top five percent most frequent tokens in the text.

  • tokenpositionrelative: In a line, the number of tokens before / the total number of tokens in this line.

  • tokeniscapitalized: The first letter of this word is capitalized.

  • tokenisupper: The word is upper-cased.

  • tokenmostfrequentpredecessor: The token is among the top five percent most frequent predecessors before a named entity.

  • tokeninacronymlist: The token is an acronym, for example, EU, UN, PS, etc.

  • tokeningeonames: This token appears in the list of geographic names.

Relative position: This is the relative positional composition of feature. This must be a string of numbers separated by comma:

  • 0 is for the current feature,

  • 1 is for the next feature; and so on.

For example -2,-1,0,1,2 means that you use the current token, the preceding two and the following two context tokens as features.

Additional Features

Select this check box to add additional features in the Additional feature template.

NLP Library

From this list, select the library to be used between ScalaNLP and Stanford CoreNLP.

If the input is a text preprocessed using the tNLPPreprocessing component, select the same NLP Library that was used for the preprocessing.

Model location

Select the Save the model on file system check box and in the Folder field, set the path to the local folder where you want to generate the model file.

If you want to store the model in a specific file system, for example S3 or HDFS, you must use the corresponding component in the Job and select the Define a storage configuration component check box in the component basic settings.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

Run cross validation evaluation

If you select this check box, the tNLPModel component will not generate a model file. Instead, it will run a K-fold cross-validation to evaluate the performance of the model.

The cross-validation process is repeated K times according to the Fold parameter.

After evaluating the model, clear this check box and rerun the Job to generate the model file.


Usage rule

This component is used as an end component and requires an input link.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Spark Batch Connection

You need to use the Spark Configuration tab in the Run view to 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: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory.

  • Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.