Package opennlp.tools.lemmatizer

# Class LemmatizerME

java.lang.Object
opennlp.tools.lemmatizer.LemmatizerME

All Implemented Interfaces:
  `Lemmatizer`, `Probabilistic`

public class LemmatizerME extends Object implements Lemmatizer, Probabilistic
A probabilistic `Lemmatizer` implementation.

Tries to predict the induced permutation class for each word depending on its surrounding context.

Based on Grzegorz Chrupała. 2008. Towards a Machine-Learning Architecture for Lexical Functional Grammar Parsing. PhD dissertation, Dublin City University

See Also:
  - `Lemmatizer`
  - `Probabilistic`

- ## Field Summary

  Fields
  Modifier and Type
  Field
  Description
  `static final int`
  `DEFAULT_BEAM_SIZE`
  `static final int`
  `LEMMA_NUMBER`

- ## Constructor Summary

  Constructors
  Constructor
  Description
  `LemmatizerME(LemmatizerModel model)`
  Initializes a `LemmatizerME` with the provided `model` and a default `beam size` of `3`.

- ## Method Summary

  Modifier and Type
  Method
  Description
  `static String[]`
  `decodeLemmas(String[] toks, String[] preds)`
  Decodes the lemma from the word and the induced lemma class.
  `static String[]`
  `encodeLemmas(String[] toks, String[] lemmas)`
  Encodes the word given its lemmas.
  `String[]`
  `lemmatize(String[] toks, String[] tags)`
  Generates lemmas for the word and postag.
  `List<List<String>>`
  `lemmatize(List<String> toks, List<String> tags)`
  Generates lemma tags for the word and postag.
  `String[][]`
  `predictLemmas(int numLemmas, String[] toks, String[] tags)`
  Predict all possible lemmas (using a default upper bound).
  `String[]`
  `predictSES(String[] toks, String[] tags)`
  Predict Short Edit Script (automatically induced lemma class).
  `double[]`
  `probs()`
  Retrieves the probabilities of the last decoded sequence.
  `void`
  `probs(double[] probs)`
  Populates the specified array with the probabilities of the last decoded sequence.
  `Sequence[]`
  `topKLemmaClasses(String[] sentence, String[] tags)`
  `Sequence[]`
  `topKLemmaClasses(String[] sentence, String[] tags, double minSequenceScore)`
  `Sequence[]`
  `topKSequences(String[] sentence, String[] tags)`
  `Sequence[]`
  `topKSequences(String[] sentence, String[] tags, double minSequenceScore)`
  `static LemmatizerModel`
  `train(String languageCode, ObjectStream<LemmaSample> samples, TrainingParameters params, LemmatizerFactory factory)`
  Starts a training of a `LemmatizerModel` with the given parameters.

  ### Methods inherited from class java.lang.Object

  `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- ## Field Details

  - ### LEMMA_NUMBER

    public static final int LEMMA_NUMBER
    See Also:
      - Constant Field Values

  - ### DEFAULT_BEAM_SIZE

    public static final int DEFAULT_BEAM_SIZE
    See Also:
      - Constant Field Values

- ## Constructor Details

  - ### LemmatizerME

    public LemmatizerME(LemmatizerModel model)
    Initializes a `LemmatizerME` with the provided `model` and a default `beam size` of `3`.
    Parameters:
      `model` - The `LemmatizerModel` to be used.

- ## Method Details

  - ### lemmatize

    public String[] lemmatize(String[] toks, String[] tags)
    Description copied from interface: `Lemmatizer`
    Generates lemmas for the word and postag.
    Specified by:
      `lemmatize` in interface `Lemmatizer`
    Parameters:
      `toks` - An array of the tokens
      `tags` - an array of the pos tags
    Returns:
      An array of possible lemmas for each token in the `toks` sequence.

  - ### lemmatize

    public List<List<String>> lemmatize(List<String> toks, List<String> tags)
    Description copied from interface: `Lemmatizer`
    Generates lemma tags for the word and postag.
    Specified by:
      `lemmatize` in interface `Lemmatizer`
    Parameters:
      `toks` - An array of the tokens
      `tags` - An array of the pos tags
    Returns:
      A list of every possible lemma for each token in the `toks` sequence.

  - ### predictSES

    public String[] predictSES(String[] toks, String[] tags)
    Predict Short Edit Script (automatically induced lemma class).
    Parameters:
      `toks` - An array of tokens.
      `tags` - An array of postags.
    Returns:
      An array of possible lemma classes for each token in `toks`.

  - ### predictLemmas

    public String[][] predictLemmas(int numLemmas, String[] toks, String[] tags)
    Predict all possible lemmas (using a default upper bound).
    Parameters:
      `numLemmas` - The default number of lemmas
      `toks` - An array of tokens.
      `tags` - An array of postags.
    Returns:
      A 2-dimensional array containing all possible lemmas for each token and postag pair.

  - ### decodeLemmas

    public static String[] decodeLemmas(String[] toks, String[] preds)
    Decodes the lemma from the word and the induced lemma class.
    Parameters:
      `toks` - An array of tokens.
      `preds` - An array of predicted lemma classes.
    Returns:
      The array of decoded lemmas.

  - ### encodeLemmas

    public static String[] encodeLemmas(String[] toks, String[] lemmas)
    Encodes the word given its lemmas.
    Parameters:
      `toks` - An array of tokens.
      `lemmas` - An array of lemmas.
    Returns:
      The array of lemma classes.

  - ### topKSequences

    public Sequence[] topKSequences(String[] sentence, String[] tags)
    Parameters:
      `sentence` - An array of tokens.
      `tags` - An array of postags.
    Returns:
      Retrieves the top-k `sequences`.

  - ### topKSequences

    public Sequence[] topKSequences(String[] sentence, String[] tags, double minSequenceScore)
    Parameters:
      `sentence` - An array of tokens.
      `tags` - An array of postags.
      `minSequenceScore` - The minimum score to be achieved.
    Returns:
      Retrieves the top-k `sequences`.

  - ### probs

    public void probs(double[] probs)
    Populates the specified array with the probabilities of the last decoded sequence. The sequence was determined based on the previous call to `lemmatize(String[], String[])`.

    The specified array should be at least as large as the number of tokens in the previous call to `lemmatize(String[], String[])`.

    Parameters:
      `probs` - An array used to hold the probabilities of the last decoded sequence.

  - ### probs

    public double[] probs()
    Retrieves the probabilities of the last decoded sequence. The sequence was determined based on the previous call to `lemmatize(String[], String[])`.
    Specified by:
      `probs` in interface `Probabilistic`
    Returns:
      An array with the same number of probabilities as tokens were sent to `lemmatize(String[], String[])` when it was last called.

  - ### train

    public static LemmatizerModel train(String languageCode, ObjectStream<LemmaSample> samples, TrainingParameters params, LemmatizerFactory factory) throws IOException
    Starts a training of a `LemmatizerModel` with the given parameters.
    Parameters:
      `languageCode` - The ISO conform language code.
      `samples` - The `ObjectStream` of `LemmaSample` used as input for training.
      `params` - The `TrainingParameters` for the context of the training.
      `factory` - The `LemmatizerFactory` for creating related objects defined via `params`.
    Returns:
      A valid, trained `LemmatizerModel` instance.
    Throws:
      `IOException` - Thrown if IO errors occurred.

  - ### topKLemmaClasses

    public Sequence[] topKLemmaClasses(String[] sentence, String[] tags)
    Parameters:
      `sentence` - An array of tokens.
      `tags` - An array of postags.
    Returns:
      Retrieves the top-k `lemma classes`.

  - ### topKLemmaClasses

    public Sequence[] topKLemmaClasses(String[] sentence, String[] tags, double minSequenceScore)
    Parameters:
      `sentence` - An array of tokens.
      `tags` - An array of postags.
      `minSequenceScore` - The minimum score to be achieved.
    Returns:
      Retrieves the top-k `lemma classes`.