edu.stanford.nlp.parser.lexparser

Class ChineseLexiconAndWordSegmenter

java.lang.Object

edu.stanford.nlp.parser.lexparser.ChineseLexiconAndWordSegmenter

All Implemented Interfaces:

Lexicon, WordSegmenter, java.io.Serializable

public class ChineseLexiconAndWordSegmenter
extends java.lang.Object
implements Lexicon, WordSegmenter

This class lets you train a lexicon and segmenter at the same time.

Author:

Galen Andrew, Pi-Chuan Chang

See Also:

Serialized Form

Field Summary

Fields inherited from interface edu.stanford.nlp.parser.lexparser.Lexicon

BOUNDARY, BOUNDARY_TAG, UNKNOWN_WORD

Constructor Summary

Constructors

Constructor and Description
ChineseLexiconAndWordSegmenter(ChineseLexicon lex, WordSegmenter seg)
ChineseLexiconAndWordSegmenter(java.lang.String segmenterFileOrUrl, Options op) Construct a new ChineseLexiconAndWordSegmenter.

Method Summary

Modifier and Type	Method and Description
void	finishTraining() Done collecting statistics for the lexicon.
static ChineseLexiconAndWordSegmenter	getSegmenterDataFromFile(java.lang.String parserFileOrUrl, Options op)
protected static ChineseLexiconAndWordSegmenter	getSegmenterDataFromSerializedFile(java.lang.String serializedFileOrUrl)
UnknownWordModel	getUnknownWordModel()
void	incrementTreesRead(double weight) If training on a per-word basis instead of on a per-tree basis, we will want to increment the tree count as this happens.
void	initializeTraining(double numTrees) Start training this lexicon on the expected number of trees.
boolean	isKnown(int word) Checks whether a word is in the lexicon.
boolean	isKnown(java.lang.String word) Checks whether a word is in the lexicon.
void	loadSegmenter(java.lang.String filename)
static void	main(java.lang.String[] args) This method lets you train and test a segmenter relative to a Treebank.
int	numRules() Returns the number of rules (tag rewrites as word) in the Lexicon.
void	readData(java.io.BufferedReader in) Read the lexicon from the BufferedReader in the format written by writeData.
java.util.Iterator<IntTaggedWord>	ruleIteratorByWord(int word, int loc, java.lang.String featureSpec) Get an iterator over all rules (pairs of (word, POS)) for this word.
java.util.Iterator<IntTaggedWord>	ruleIteratorByWord(java.lang.String word, int loc, java.lang.String featureSpec) Same thing, but with a string that needs to be translated by the lexicon's word index
float	score(IntTaggedWord iTW, int loc, java.lang.String word, java.lang.String featureSpec) Get the score of this word with this tag (as an IntTaggedWord) at this loc.
java.util.List<HasWord>	segment(java.lang.String s)
void	setUnknownWordModel(UnknownWordModel uwm)
java.util.Set<java.lang.String>	tagSet(java.util.function.Function<java.lang.String,java.lang.String> basicCategoryFunction) Return the Set of tags used by this tagger (available after training the tagger).
void	train(java.util.Collection<Tree> trees) Trains this lexicon on the Collection of trees.
void	train(java.util.Collection<Tree> trees, java.util.Collection<Tree> rawTrees)
void	train(java.util.Collection<Tree> trees, double weight)
void	train(java.util.List<TaggedWord> sentence)
void	train(java.util.List<TaggedWord> sentence, double weight) Not all subclasses support this particular method.
void	train(TaggedWord tw, int loc, double weight) Not all subclasses support this particular method.
void	train(Tree tree)
void	train(Tree tree, double weight)
void	trainUnannotated(java.util.List<TaggedWord> sentence, double weight) Sometimes we might have a sentence of tagged words which we would like to add to the lexicon, but they weren't part of a binarized, markovized, or otherwise annotated tree.
void	writeData(java.io.Writer w) Write the lexicon in human-readable format to the Writer.

Methods inherited from class java.lang.Object

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

Constructor Detail

ChineseLexiconAndWordSegmenter

public ChineseLexiconAndWordSegmenter(ChineseLexicon lex,
                                      WordSegmenter seg)

ChineseLexiconAndWordSegmenter

public ChineseLexiconAndWordSegmenter(java.lang.String segmenterFileOrUrl,
                                      Options op)

Construct a new ChineseLexiconAndWordSegmenter. This loads a segmenter file that was previously assembled and stored.

Throws:

java.lang.IllegalArgumentException - If segmenter data cannot be loaded

Method Detail

segment

public java.util.List<HasWord> segment(java.lang.String s)

Specified by:

segment in interface WordSegmenter

isKnown

public boolean isKnown(int word)

Description copied from interface: Lexicon

Checks whether a word is in the lexicon.

Specified by:

isKnown in interface Lexicon

Parameters:

word - The word as an int

Returns:

Whether the word is in the lexicon

isKnown

public boolean isKnown(java.lang.String word)

Description copied from interface: Lexicon

Checks whether a word is in the lexicon.

Specified by:

isKnown in interface Lexicon

Parameters:

word - The word as a String

Returns:

Whether the word is in the lexicon

tagSet

public java.util.Set<java.lang.String> tagSet(java.util.function.Function<java.lang.String,java.lang.String> basicCategoryFunction)

Return the Set of tags used by this tagger (available after training the tagger).

Specified by:

tagSet in interface Lexicon

Returns:

The Set of tags used by this tagger

ruleIteratorByWord

public java.util.Iterator<IntTaggedWord> ruleIteratorByWord(int word,
                                                            int loc,
                                                            java.lang.String featureSpec)

Description copied from interface: Lexicon

Get an iterator over all rules (pairs of (word, POS)) for this word.

Specified by:

ruleIteratorByWord in interface Lexicon

Parameters:

word - The word, represented as an integer in Index

loc - The position of the word in the sentence (counting from 0). Implementation note: The BaseLexicon class doesn't actually make use of this position information.

featureSpec - Additional word features like morphosyntactic information.

Returns:

An Iterator over a List ofIntTaggedWords, which pair the word with possible taggings as integer pairs. (Each can be thought of as a tag -> word rule.)

ruleIteratorByWord

public java.util.Iterator<IntTaggedWord> ruleIteratorByWord(java.lang.String word,
                                                            int loc,
                                                            java.lang.String featureSpec)

Description copied from interface: Lexicon

Same thing, but with a string that needs to be translated by the lexicon's word index

Specified by:

ruleIteratorByWord in interface Lexicon

numRules

public int numRules()

Returns the number of rules (tag rewrites as word) in the Lexicon. This method assumes that the lexicon has been initialized.

Specified by:

numRules in interface Lexicon

Returns:

The number of rules (tag rewrites as word) in the Lexicon.

initializeTraining

public void initializeTraining(double numTrees)

Description copied from interface: Lexicon

Start training this lexicon on the expected number of trees. (Some UnknownWordModels use the number of trees to know when to start counting statistics.)

Specified by:

initializeTraining in interface Lexicon

Specified by:

initializeTraining in interface WordSegmenter

train

public void train(java.util.Collection<Tree> trees)

Description copied from interface: Lexicon

Trains this lexicon on the Collection of trees. Can be called more than once with different collections of trees.

Specified by:

train in interface Lexicon

Specified by:

train in interface WordSegmenter

Parameters:

trees - Trees to train on

train

public void train(java.util.Collection<Tree> trees,
                  double weight)

Specified by:

train in interface Lexicon

train

public void train(Tree tree)

Specified by:

train in interface WordSegmenter

train

public void train(Tree tree,
                  double weight)

Specified by:

train in interface Lexicon

train

public void train(java.util.List<TaggedWord> sentence)

Specified by:

train in interface WordSegmenter

train

public void train(java.util.List<TaggedWord> sentence,
                  double weight)

Description copied from interface: Lexicon

Not all subclasses support this particular method. Those that don't will barf...

Specified by:

train in interface Lexicon

trainUnannotated

public void trainUnannotated(java.util.List<TaggedWord> sentence,
                             double weight)

Description copied from interface: Lexicon

Sometimes we might have a sentence of tagged words which we would like to add to the lexicon, but they weren't part of a binarized, markovized, or otherwise annotated tree.

Specified by:

trainUnannotated in interface Lexicon

incrementTreesRead

public void incrementTreesRead(double weight)

Description copied from interface: Lexicon

If training on a per-word basis instead of on a per-tree basis, we will want to increment the tree count as this happens.

Specified by:

incrementTreesRead in interface Lexicon

train

public void train(TaggedWord tw,
                  int loc,
                  double weight)

Description copied from interface: Lexicon

Not all subclasses support this particular method. Those that don't will barf...

Specified by:

train in interface Lexicon

finishTraining

public void finishTraining()

Description copied from interface: Lexicon

Done collecting statistics for the lexicon.

Specified by:

finishTraining in interface Lexicon

Specified by:

finishTraining in interface WordSegmenter

score

public float score(IntTaggedWord iTW,
                   int loc,
                   java.lang.String word,
                   java.lang.String featureSpec)

Description copied from interface: Lexicon

Get the score of this word with this tag (as an IntTaggedWord) at this loc. (Presumably an estimate of P(word | tag).)

Specified by:

score in interface Lexicon

Parameters:

iTW - An IntTaggedWord pairing a word and POS tag

loc - The position in the sentence. In the default implementation this is used only for unknown words to change their probability distribution when sentence initial.

word - The word itself; useful so we don't have to look it up in an index

featureSpec - TODO

Returns:

A score, usually, log P(word|tag)

loadSegmenter

public void loadSegmenter(java.lang.String filename)

Specified by:

loadSegmenter in interface WordSegmenter

readData

public void readData(java.io.BufferedReader in)
              throws java.io.IOException

Description copied from interface: Lexicon

Read the lexicon from the BufferedReader in the format written by writeData. (An optional operation.)

Specified by:

readData in interface Lexicon

Parameters:

in - The BufferedReader to read from

Throws:

java.io.IOException - If any I/O problem

writeData

public void writeData(java.io.Writer w)
               throws java.io.IOException

Description copied from interface: Lexicon

Write the lexicon in human-readable format to the Writer. (An optional operation.)

Specified by:

writeData in interface Lexicon

Parameters:

w - The writer to output to

Throws:

java.io.IOException - If any I/O problem

getSegmenterDataFromFile

public static ChineseLexiconAndWordSegmenter getSegmenterDataFromFile(java.lang.String parserFileOrUrl,
                                                                      Options op)

getSegmenterDataFromSerializedFile

protected static ChineseLexiconAndWordSegmenter getSegmenterDataFromSerializedFile(java.lang.String serializedFileOrUrl)

main

public static void main(java.lang.String[] args)

This method lets you train and test a segmenter relative to a Treebank.

Implementation note: This method is largely cloned from LexicalizedParser's main method. Should we try to have it be able to train segmenters to stop things going out of sync?

getUnknownWordModel

public UnknownWordModel getUnknownWordModel()

Specified by:

getUnknownWordModel in interface Lexicon

setUnknownWordModel

public void setUnknownWordModel(UnknownWordModel uwm)

Specified by:

setUnknownWordModel in interface Lexicon

train

public void train(java.util.Collection<Tree> trees,
                  java.util.Collection<Tree> rawTrees)

Specified by:

train in interface Lexicon