org.tribuo.data.text.impl

Class TokenPipeline

java.lang.Object

org.tribuo.data.text.impl.TokenPipeline

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>, TextPipeline

public class TokenPipeline
extends Object
implements TextPipeline

A pipeline for generating ngram features.

Constructor Summary

Constructors

Constructor and Description
TokenPipeline(Tokenizer tokenizer, int ngram, boolean termCounting) Creates a new token pipeline.
TokenPipeline(Tokenizer tokenizer, int ngram, boolean termCounting, int dimension) Creates a new token pipeline.

Method Summary

Modifier and Type	Method and Description
com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance	getProvenance()
void	postConfig() Used by the OLCUT configuration system, and should not be called by external code.
List<Feature>	process(String tag, String data) Extracts a list of features from the supplied text, using the tag to prepend the feature names.
String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

TokenPipeline

public TokenPipeline(Tokenizer tokenizer,
                     int ngram,
                     boolean termCounting)

Creates a new token pipeline.

Parameters:

tokenizer - The tokenizer to use to split up the text into words (i.e., features.)

ngram - The maximum size of ngram features to add to the features generated by the pipeline. A value of n means that ngram features of size 1-n will be generated. A good standard value to use is 2, which means that unigram and bigram features will be generated. You will very likely see diminishing returns for larger values of n but there will be times when they will be necessary.

termCounting - If true, multiple occurrences of terms in the document will be counted and the count will be used as the value of the features that are produced.

TokenPipeline

public TokenPipeline(Tokenizer tokenizer,
                     int ngram,
                     boolean termCounting,
                     int dimension)

Creates a new token pipeline.

Parameters:

tokenizer - The tokenizer to use to split up the text into words (i.e., features.)

ngram - The maximum size of ngram features to add to the features generated by the pipeline. A value of n means that ngram features of size 1-n will be generated. A good standard value to use is 2, which means that unigram and bigram features will be generated. You will very likely see diminishing returns for larger values of n but there will be times when they will be necessary.

termCounting - If true, multiple occurrences of terms in the document will be counted and the count will be used as the value of the features that are produced.

dimension - The maximum dimension for the feature space. If this value is greater than 0, then at most dimension features will be through the use of a hashing function that will collapse the feature space.

Method Detail

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

toString

public String toString()

Overrides:

toString in class Object

process

public List<Feature> process(String tag,
                             String data)

Description copied from interface: TextPipeline

Extracts a list of features from the supplied text, using the tag to prepend the feature names.

Specified by:

process in interface TextPipeline

Parameters:

tag - The feature name tag.

data - The text to extract.

Returns:

The extracted features.

getProvenance

public com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>