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"""Contains classes for the WordSegmenter."""
from sparknlp.common import *
[docs]class WordSegmenterApproach(AnnotatorApproach):
"""Trains a WordSegmenter which tokenizes non-english or non-whitespace
separated texts.
Many languages are not whitespace separated and their sentences are a concatenation
of many symbols, like Korean, Japanese or Chinese. Without understanding the
language, splitting the words into their corresponding tokens is impossible. The
WordSegmenter is trained to understand these languages and split them into
semantically correct parts.
This annotator is based on the paper Chinese Word Segmentation as Character Tagging
[1]. Word segmentation is treated as a tagging problem. Each character is be tagged
as on of four different labels: LL (left boundary), RR (right boundary), MM (middle)
and LR (word by itself). The label depends on the position of the word in the
sentence. LL tagged words will combine with the word on the right. Likewise, RR
tagged words combine with words on the left. MM tagged words are treated as the
middle of the word and combine with either side. LR tagged words are words by
themselves.
Example (from [1], Example 3(a) (raw), 3(b) (tagged), 3(c) (translation)):
- 上海 计划 到 本 世纪 末 实现 人均 国内 生产 总值 五千 美元
- 上/LL 海/RR 计/LL 划/RR 到/LR 本/LR 世/LL 纪/RR 末/LR 实/LL 现/RR 人/LL 均/RR
国/LL 内/RR 生/LL 产/RR 总/LL值/RR 五/LL 千/RR 美/LL 元/RR
- Shanghai plans to reach the goal of 5,000 dollars in per capita GDP by the end
of the century.
For instantiated/pretrained models, see :class:`.WordSegmenterModel`.
To train your own model, a training dataset consisting of `Part-Of-Speech
tags <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__ is required.
The data has to be loaded into a dataframe, where the column is an
Annotation of type ``POS``. This can be set with :meth:`.setPosColumn`.
**Tip**:
The helper class :class:`.POS` might be useful to read training data into
data frames.
For extended examples of usage, see the `Examples
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/chinese/word_segmentation>`__.
References
----------
`[1] <https://aclanthology.org/O03-4002.pdf>`__ Xue, Nianwen. “Chinese Word
Segmentation as Character Tagging.” International Journal of Computational
Linguistics & Chinese Language Processing, Volume 8, Number 1, February 2003:
Special Issue on Word Formation and Chinese Language Processing, 2003, pp. 29-48.
ACLWeb, https://aclanthology.org/O03-4002.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``TOKEN``
====================== ======================
Parameters
----------
posCol
column of Array of POS tags that match tokens
nIterations
Number of iterations in training, converges to better accuracy, by
default 5
frequencyThreshold
How many times at least a tag on a word to be marked as frequent, by
default 5
ambiguityThreshold
How much percentage of total amount of words are covered to be marked as
frequent, by default 0.97
enableRegexTokenizer
Whether to use RegexTokenizer before segmentation. Useful for multilingual text
toLowercase
Indicates whether to convert all characters to lowercase before tokenizing. Used only when enableRegexTokenizer is true
pattern
regex pattern used for tokenizing. Used only when enableRegexTokenizer is true
Examples
--------
In this example, ``"chinese_train.utf8"`` is in the form of::
十|LL 四|RR 不|LL 是|RR 四|LL 十|RR
and is loaded with the `POS` class to create a dataframe of ``POS`` type
Annotations.
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> wordSegmenter = WordSegmenterApproach() \\
... .setInputCols(["document"]) \\
... .setOutputCol("token") \\
... .setPosColumn("tags") \\
... .setNIterations(5)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... wordSegmenter
... ])
>>> trainingDataSet = POS().readDataset(
... spark,
... "src/test/resources/word-segmenter/chinese_train.utf8"
... )
>>> pipelineModel = pipeline.fit(trainingDataSet)
"""
name = "WordSegmenterApproach"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.TOKEN
posCol = Param(Params._dummy(),
"posCol",
"column of Array of POS tags that match tokens",
typeConverter=TypeConverters.toString)
nIterations = Param(Params._dummy(),
"nIterations",
"Number of iterations in training, converges to better accuracy",
typeConverter=TypeConverters.toInt)
frequencyThreshold = Param(Params._dummy(),
"frequencyThreshold",
"How many times at least a tag on a word to be marked as frequent",
typeConverter=TypeConverters.toInt)
ambiguityThreshold = Param(Params._dummy(),
"ambiguityThreshold",
"How much percentage of total amount of words are covered to be marked as frequent",
typeConverter=TypeConverters.toFloat)
enableRegexTokenizer = Param(Params._dummy(),
"enableRegexTokenizer",
"Whether to use RegexTokenizer before segmentation. Useful for multilingual text",
typeConverter=TypeConverters.toBoolean)
toLowercase = Param(Params._dummy(),
"toLowercase",
"Indicates whether to convert all characters to lowercase before tokenizing.",
typeConverter=TypeConverters.toBoolean)
pattern = Param(Params._dummy(),
"pattern",
"regex pattern used for tokenizing. Defaults \\s+",
typeConverter=TypeConverters.toString)
@keyword_only
def __init__(self):
super(WordSegmenterApproach, self).__init__(
classname="com.johnsnowlabs.nlp.annotators.ws.WordSegmenterApproach")
self._setDefault(
nIterations=5, frequencyThreshold=5, ambiguityThreshold=0.97,
enableRegexTokenizer=False, toLowercase=False, pattern="\\s+"
)
[docs] def setPosColumn(self, value):
"""Sets column name for array of POS tags that match tokens.
Parameters
----------
value : str
Name of the column
"""
return self._set(posCol=value)
[docs] def setNIterations(self, value):
"""Sets number of iterations in training, converges to better accuracy,
by default 5.
Parameters
----------
value : int
Number of iterations
"""
return self._set(nIterations=value)
[docs] def setFrequencyThreshold(self, value):
"""Sets how many times at least a tag on a word to be marked as
frequent, by default 5.
Parameters
----------
value : int
Frequency threshold to be marked as frequent
"""
return self._set(frequencyThreshold=value)
[docs] def setAmbiguityThreshold(self, value):
"""Sets the percentage of total amount of words are covered to be
marked as frequent, by default 0.97.
Parameters
----------
value : float
Percentage of total amount of words are covered to be
marked as frequent
"""
return self._set(ambiguityThreshold=value)
[docs] def getNIterations(self):
"""Gets number of iterations in training, converges to better accuracy.
Returns
-------
int
Number of iterations
"""
return self.getOrDefault(self.nIterations)
[docs] def getFrequencyThreshold(self):
"""Sets How many times at least a tag on a word to be marked as
frequent.
Returns
-------
int
Frequency threshold to be marked as frequent
"""
return self.getOrDefault(self.frequencyThreshold)
[docs] def getAmbiguityThreshold(self):
"""Sets How much percentage of total amount of words are covered to be
marked as frequent.
Returns
-------
float
Percentage of total amount of words are covered to be
marked as frequent
"""
return self.getOrDefault(self.ambiguityThreshold)
[docs] def setEnableRegexTokenizer(self, value):
"""Sets whether to to use RegexTokenizer before segmentation.
Useful for multilingual text
Parameters
----------
value : bool
Whether to use RegexTokenizer before segmentation
"""
return self._set(enableRegexTokenizer=value)
[docs] def setToLowercase(self, value):
"""Sets whether to convert all characters to lowercase before
tokenizing, by default False.
Parameters
----------
value : bool
Whether to convert all characters to lowercase before tokenizing
"""
return self._set(toLowercase=value)
[docs] def setPattern(self, value):
"""Sets the regex pattern used for tokenizing, by default ``\\s+``.
Parameters
----------
value : str
Regex pattern used for tokenizing
"""
return self._set(pattern=value)
def _create_model(self, java_model):
return WordSegmenterModel(java_model=java_model)
[docs]class WordSegmenterModel(AnnotatorModel):
"""WordSegmenter which tokenizes non-english or non-whitespace separated
texts.
Many languages are not whitespace separated and their sentences are a
concatenation of many symbols, like Korean, Japanese or Chinese. Without
understanding the language, splitting the words into their corresponding
tokens is impossible. The WordSegmenter is trained to understand these
languages and plit them into semantically correct parts.
This is the instantiated model of the :class:`.WordSegmenterApproach`. For
training your own model, please see the documentation of that class.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> wordSegmenter = WordSegmenterModel.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("words_segmented")
The default model is ``"wordseg_pku"``, default language is ``"zh"``, if no
values are provided. For available pretrained models please see the `Models
Hub <https://sparknlp.org/models?task=Word+Segmentation>`__.
For extended examples of usage, see the `Examples
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/jupyter/annotation/chinese/word_segmentation/words_segmenter_demo.ipynb>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``TOKEN``
====================== ======================
Parameters
----------
None
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> wordSegmenter = WordSegmenterModel.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("token")
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... wordSegmenter
... ])
>>> data = spark.createDataFrame([["然而，這樣的處理也衍生了一些問題。"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("token.result").show(truncate=False)
+--------------------------------------------------------+
|result |
+--------------------------------------------------------+
|[然而, ，, 這樣, 的, 處理, 也, 衍生, 了, 一些, 問題, 。 ]|
+--------------------------------------------------------+
"""
name = "WordSegmenterModel"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.TOKEN
enableRegexTokenizer = Param(Params._dummy(),
"enableRegexTokenizer",
"Whether to use RegexTokenizer before segmentation. Useful for multilingual text",
typeConverter=TypeConverters.toBoolean)
toLowercase = Param(Params._dummy(),
"toLowercase",
"Indicates whether to convert all characters to lowercase before tokenizing.",
typeConverter=TypeConverters.toBoolean)
pattern = Param(Params._dummy(),
"pattern",
"regex pattern used for tokenizing. Defaults \\s+",
typeConverter=TypeConverters.toString)
[docs] def setEnableRegexTokenizer(self, value):
"""Sets whether to to use RegexTokenizer before segmentation.
Useful for multilingual text
Parameters
----------
value : bool
Whether to use RegexTokenizer before segmentation
"""
return self._set(enableRegexTokenizer=value)
[docs] def setToLowercase(self, value):
"""Sets whether to convert all characters to lowercase before
tokenizing, by default False.
Parameters
----------
value : bool
Whether to convert all characters to lowercase before tokenizing
"""
return self._set(toLowercase=value)
[docs] def setPattern(self, value):
"""Sets the regex pattern used for tokenizing, by default ``\\s+``.
Parameters
----------
value : str
Regex pattern used for tokenizing
"""
return self._set(pattern=value)
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.ws.WordSegmenterModel", java_model=None):
super(WordSegmenterModel, self).__init__(
classname=classname,
java_model=java_model
)
@staticmethod
[docs] def pretrained(name="wordseg_pku", lang="zh", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "wordseg_pku"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
WordSegmenterModel
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(WordSegmenterModel, name, lang, remote_loc)