de.jungblut.ner

Class SparseFeatureExtractorHelper<K>

java.lang.Object

de.jungblut.ner.SparseFeatureExtractorHelper<K>

public final class SparseFeatureExtractorHelper<K>
extends Object

Convenient helper for creating vectors out of text features for sequence learning. Inspired by Coursera's NLP Class PA4.

Author:

thomas.jungblut

Constructor Summary

Constructors

Constructor and Description
SparseFeatureExtractorHelper(List<K> words, List<Integer> labels, SequenceFeatureExtractor<K> extractor) Constructs this feature factory.
SparseFeatureExtractorHelper(List<K> words, List<Integer> labels, SequenceFeatureExtractor<K> extractor, String[] dictionary) Constructs this feature factory via a given dictionary.

Method Summary

Modifier and Type	Method and Description
String[]	getDictionary()
de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector[],de.jungblut.math.DoubleVector[]>	vectorize() Vectorizes the given data from the constructor.
de.jungblut.math.DoubleVector	vectorize(K word) Vectorizes the given word.
de.jungblut.math.DoubleVector	vectorize(K word, Integer lastLabel) Vectorizes the given word with the previous outcome.
de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector[],de.jungblut.math.DoubleVector[]>	vectorize(List<K> words, List<Integer> labels) Vectorizes the given data.
de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector[],de.jungblut.math.DoubleVector[]>	vectorizeAdditionals(List<K> words, List<Integer> labels) Vectorizes the given data.
de.jungblut.math.DoubleVector[]	vectorizeEachLabel(List<K> words) Vectorizes the given data for each label.

Methods inherited from class java.lang.Object

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

Constructor Detail

SparseFeatureExtractorHelper

public SparseFeatureExtractorHelper(List<K> words,
                                    List<Integer> labels,
                                    SequenceFeatureExtractor<K> extractor)

Constructs this feature factory.

Parameters:

words - a list of words in sequence to learn on.

labels - the corresponding labels in parallel to the words.

extractor - the core implementation of the feature extractor.

SparseFeatureExtractorHelper

public SparseFeatureExtractorHelper(List<K> words,
                                    List<Integer> labels,
                                    SequenceFeatureExtractor<K> extractor,
                                    String[] dictionary)

Constructs this feature factory via a given dictionary.

Parameters:

words - a list of words in sequence to learn on.

labels - the corresponding labels in parallel to the words.

extractor - the core implementation of the feature extractor.

dictionary - an already given dictionary.

Method Detail

vectorize

public de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector[],de.jungblut.math.DoubleVector[]> vectorize()

Vectorizes the given data from the constructor. Internally builds a dictionary that can be saved to vectorize additional data with vectorizeAdditionals(List, List).

Returns:

a Tuple with the features in the first dimension, and on the second the outcome.

vectorize

public de.jungblut.math.DoubleVector vectorize(K word)

Vectorizes the given word.

Returns:

the feature for the given word.

vectorize

public de.jungblut.math.DoubleVector vectorize(K word,
                                               Integer lastLabel)

Vectorizes the given word with the previous outcome.

Returns:

the feature for the given word.

vectorize

public de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector[],de.jungblut.math.DoubleVector[]> vectorize(List<K> words,
                                                                                                               List<Integer> labels)

Vectorizes the given data. Internally uses a dictionary that was created by vectorize() or creates one on this data.

Returns:

a Tuple with the features in the first dimension, and on the second the outcome.

vectorizeAdditionals

public de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector[],de.jungblut.math.DoubleVector[]> vectorizeAdditionals(List<K> words,
                                                                                                                          List<Integer> labels)

Vectorizes the given data. Internally uses a dictionary that was created by vectorize() or creates one on this data.

Returns:

a Tuple with the features in the first dimension, and on the second the outcome.

vectorizeEachLabel

public de.jungblut.math.DoubleVector[] vectorizeEachLabel(List<K> words)

Vectorizes the given data for each label. Internally uses a dictionary that was created by vectorize() or creates one on this data.

getDictionary

public String[] getDictionary()

Returns:

the built dictionary.