Package opennlp.tools.chunker

# Class ChunkerME

java.lang.Object
opennlp.tools.chunker.ChunkerME

All Implemented Interfaces:
  `Chunker`, `Probabilistic`

public class ChunkerME extends Object implements Chunker, Probabilistic
The class represents a maximum-entropy-based `Chunker`. A chunker can be used to find flat structures based on sequence inputs such as noun phrases or named entities.

See Also:
  - `Chunker`
  - `Probabilistic`

- ## Field Summary

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

- ## Constructor Summary

  Constructors
  Constructor
  Description
  `ChunkerME(String language)`
  Initializes a `Chunker` by downloading a default model.
  `ChunkerME(ChunkerModel model)`
  Initializes a `Chunker` with the specified `ChunkerModel`.

- ## Method Summary

  Modifier and Type
  Method
  Description
  `String[]`
  `chunk(String[] toks, String[] tags)`
  Generates chunk tags for the given sequence returning the result in an array.
  `Span[]`
  `chunkAsSpans(String[] toks, String[] tags)`
  Generates tagged chunk `spans` for the given sequence returning the result in a `span` array.
  `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[]`
  `topKSequences(String[] sentence, String[] tags)`
  Computes the top k chunk `sequences` for the specified sentence with the specified pos-tags.
  `Sequence[]`
  `topKSequences(String[] sentence, String[] tags, double minSequenceScore)`
  Computes the top k chunk `sequences` for the specified sentence with the specified pos-tags.
  `static ChunkerModel`
  `train(String lang, ObjectStream<ChunkSample> in, TrainingParameters mlParams, ChunkerFactory factory)`
  Starts a training of a `ChunkerModel` with the given parameters.

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

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

- ## Field Details

  - ### DEFAULT_BEAM_SIZE

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

- ## Constructor Details

  - ### ChunkerME

    public ChunkerME(String language) throws IOException
    Initializes a `Chunker` by downloading a default model.
    Parameters:
      `language` - The language of the model.
    Throws:
      `IOException` - Thrown if the model cannot be downloaded or saved.

  - ### ChunkerME

    public ChunkerME(ChunkerModel model)
    Initializes a `Chunker` with the specified `ChunkerModel`. The `DEFAULT_BEAM_SIZE` is used.
    Parameters:
      `model` - A valid `model` instance.

- ## Method Details

  - ### chunk

    public String[] chunk(String[] toks, String[] tags)
    Description copied from interface: `Chunker`
    Generates chunk tags for the given sequence returning the result in an array.
    Specified by:
      `chunk` in interface `Chunker`
    Parameters:
      `toks` - an array of the tokens or words of the sequence.
      `tags` - an array of the pos tags of the sequence.
    Returns:
      an array of chunk tags for each token in the sequence.

  - ### chunkAsSpans

    public Span[] chunkAsSpans(String[] toks, String[] tags)
    Description copied from interface: `Chunker`
    Generates tagged chunk `spans` for the given sequence returning the result in a `span` array.
    Specified by:
      `chunkAsSpans` in interface `Chunker`
    Parameters:
      `toks` - an array of the tokens or words of the sequence.
      `tags` - an array of the pos tags of the sequence.
    Returns:
      an array of `spans` with chunk tags for each chunk in the sequence.

  - ### topKSequences

    public Sequence[] topKSequences(String[] sentence, String[] tags)
    Description copied from interface: `Chunker`
    Computes the top k chunk `sequences` for the specified sentence with the specified pos-tags.
    Specified by:
      `topKSequences` in interface `Chunker`
    Parameters:
      `sentence` - The tokens of the sentence.
      `tags` - The pos-tags for the specified sentence.
    Returns:
      the top k chunk `sequences` for the specified sentence.

  - ### topKSequences

    public Sequence[] topKSequences(String[] sentence, String[] tags, double minSequenceScore)
    Description copied from interface: `Chunker`
    Computes the top k chunk `sequences` for the specified sentence with the specified pos-tags.
    Specified by:
      `topKSequences` in interface `Chunker`
    Parameters:
      `sentence` - The tokens of the sentence.
      `tags` - The pos-tags for the specified sentence.
      `minSequenceScore` - A lower bound on the score of a returned sequence.
    Returns:
      the top k chunk `sequences` for the specified sentence.

  - ### 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 `chunk`. The specified array should be at least as large as the number of tokens in the previous call to `chunk`.
    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 `chunk(String[], String[])`.
    Specified by:
      `probs` in interface `Probabilistic`
    Returns:
      An array with the same number of probabilities as tokens when `chunk(String[], String[])` was last called.

  - ### train

    public static ChunkerModel train(String lang, ObjectStream<ChunkSample> in, TrainingParameters mlParams, ChunkerFactory factory) throws IOException
    Starts a training of a `ChunkerModel` with the given parameters.
    Parameters:
      `lang` - The ISO conform language code.
      `in` - The `ObjectStream` of `ChunkSample` used as input for training.
      `mlParams` - The `TrainingParameters` for the context of the training.
      `factory` - The `ChunkerFactory` for creating related objects defined via `mlParams`.
    Returns:
      A valid, trained `ChunkerModel` instance.
    Throws:
      `IOException` - Thrown if IO errors occurred.
      `IllegalArgumentException` - Thrown if the specified `TrainerFactory.TrainerType` is not supported.