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
be processed and passed on to the next component. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see
Talend Studio

User Guide.

Repository: You have already created
the schema and stored it in the Repository. You can reuse it in various projects and
Job designs. Related topic: see
Talend Studio

User Guide.

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

This connection is effective on a per-Job basis.