Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Copyright 2017-2022 John Snow Labs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.johnsnowlabs.nlp.annotators.ws
import com.johnsnowlabs.nlp.AnnotatorType.{DOCUMENT, TOKEN}
import com.johnsnowlabs.nlp.annotators.common._
import com.johnsnowlabs.nlp.annotators.pos.perceptron.{
AveragedPerceptron,
PerceptronPredictionUtils
}
import com.johnsnowlabs.nlp.annotators.ws.TagsType.{LEFT_BOUNDARY, MIDDLE, RIGHT_BOUNDARY}
import com.johnsnowlabs.nlp.serialization.StructFeature
import com.johnsnowlabs.nlp._
import com.johnsnowlabs.nlp.annotators.RegexTokenizer
import org.apache.spark.ml.param.{BooleanParam, Param}
import org.apache.spark.ml.util.Identifiable
/** 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 annotator is based on the paper
* [[https://aclanthology.org/O03-4002.pdf Chinese Word Segmentation as Character Tagging]]. 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.
*
* This is the instantiated model of the [[WordSegmenterApproach]]. For training your own model,
* please see the documentation of that class.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val 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
* [[https://sparknlp.org/models?task=Word+Segmentation Models Hub]].
*
* For extended examples of usage, see the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/jupyter/annotation/chinese/word_segmentation/words_segmenter_demo.ipynb Examples]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/WordSegmenterTest.scala WordSegmenterTest]].
*
* '''References:'''
* - [[https://aclanthology.org/O03-4002.pdf [1]]] 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.
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotator.WordSegmenterModel
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val wordSegmenter = WordSegmenterModel.pretrained()
* .setInputCols("document")
* .setOutputCol("token")
*
* val pipeline = new Pipeline().setStages(Array(
* documentAssembler,
* wordSegmenter
* ))
*
* val data = Seq("然而，這樣的處理也衍生了一些問題。").toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.select("token.result").show(false)
* +--------------------------------------------------------+
* |result |
* +--------------------------------------------------------+
* |[然而, ，, 這樣, 的, 處理, 也, 衍生, 了, 一些, 問題, 。 ]|
* +--------------------------------------------------------+
* }}}
*
* @param uid
* required uid for storing annotator to disk
* @groupname anno Annotator types
* @groupdesc anno
* Required input and expected output annotator types
* @groupname Ungrouped Members
* @groupname param Parameters
* @groupname setParam Parameter setters
* @groupname getParam Parameter getters
* @groupname Ungrouped Members
* @groupprio param 1
* @groupprio anno 2
* @groupprio Ungrouped 3
* @groupprio setParam 4
* @groupprio getParam 5
* @groupdesc param
* A list of (hyper-)parameter keys this annotator can take. Users can set and get the
* parameter values through setters and getters, respectively.
*/
class WordSegmenterModel(override val uid: String)
extends AnnotatorModel[WordSegmenterModel]
with HasSimpleAnnotate[WordSegmenterModel]
with PerceptronPredictionUtils {
/** Annotator reference id. Used to identify elements in metadata or to refer to this annotator
* type
*/
def this() = this(Identifiable.randomUID("WORD_SEGMENTER"))
/** POS model
*
* @group param
*/
val model: StructFeature[AveragedPerceptron] =
new StructFeature[AveragedPerceptron](this, "POS Model")
val enableRegexTokenizer: BooleanParam = new BooleanParam(
this,
"enableRegexTokenizer",
"Whether to use RegexTokenizer before segmentation. Useful for multilingual text")
/** Indicates whether to convert all characters to lowercase before tokenizing (Default:
* `false`).
*
* @group param
*/
val toLowercase: BooleanParam = new BooleanParam(
this,
"toLowercase",
"Indicates whether to convert all characters to lowercase before tokenizing.\n")
/** Regex pattern used to match delimiters (Default: `"\\s+"`)
*
* @group param
*/
val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing")
/** @group getParam */
def getModel: AveragedPerceptron = $$(model)
/** @group setParam */
def setModel(targetModel: AveragedPerceptron): this.type = set(model, targetModel)
/** @group setParam */
def setEnableRegexTokenizer(value: Boolean): this.type = set(enableRegexTokenizer, value)
/** @group setParam */
def setToLowercase(value: Boolean): this.type = set(toLowercase, value)
/** @group setParam */
def setPattern(value: String): this.type = set(pattern, value)
setDefault(enableRegexTokenizer -> false, toLowercase -> false, pattern -> "\\s+")
/** takes a document and annotations and produces new annotations of this annotator's annotation
* type
*
* @param annotations
* Annotations that correspond to inputAnnotationCols generated by previous annotators if any
* @return
* any number of annotations processed for every input annotation. Not necessary one to one
* relationship
*/
override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = {
if ($(enableRegexTokenizer)) {
return segmentWithRegexAnnotator(annotations)
}
val sentences = SentenceSplit.unpack(annotations)
val tokens = getTokenAnnotations(sentences)
val tokenizedSentences = TokenizedWithSentence.unpack(annotations ++ tokens)
val tagged = tag($$(model), tokenizedSentences.toArray)
buildWordSegments(tagged)
}
private def segmentWithRegexAnnotator(annotatedSentences: Seq[Annotation]): Seq[Annotation] = {
val outputCol = Identifiable.randomUID("regex_token")
val regexTokenizer = new RegexTokenizer()
.setInputCols(getInputCols)
.setOutputCol(outputCol)
.setToLowercase($(toLowercase))
.setPattern($(pattern))
val annotatedTokens = regexTokenizer.annotate(annotatedSentences)
val segmentedResult = annotatedTokens.flatMap { annotatedToken =>
val codePoint = annotatedToken.result.codePointAt(0)
val unicodeScript = Character.UnicodeScript.of(codePoint)
if (unicodeScript == Character.UnicodeScript.LATIN) {
Seq(annotatedToken)
} else {
val sentenceIndex = annotatedToken.metadata("sentence")
val annotatedSentence = Annotation(
DOCUMENT,
annotatedToken.begin,
annotatedToken.end,
annotatedToken.result,
Map("sentence" -> sentenceIndex))
val sentence = Sentence(
annotatedToken.result,
annotatedToken.begin,
annotatedToken.end,
sentenceIndex.toInt)
val annotatedTokens = getTokenAnnotations(Seq(sentence))
val tokenizedSentences =
TokenizedWithSentence.unpack(annotatedTokens ++ Seq(annotatedSentence))
val tagged = tag($$(model), tokenizedSentences.toArray)
buildWordSegments(tagged)
}
}
segmentedResult
}
private def getTokenAnnotations(annotation: Seq[Sentence]): Seq[Annotation] = {
val tokens = annotation.flatMap { sentence =>
val chars = sentence.content.split("")
chars.zipWithIndex
.map { case (char, index) =>
val tokenIndex = index + sentence.start
Annotation(
TOKEN,
tokenIndex,
tokenIndex,
char,
Map("sentence" -> sentence.index.toString))
}
.filter(annotation => annotation.result != " ")
}
tokens
}
def buildWordSegments(taggedSentences: Array[TaggedSentence]): Seq[Annotation] = {
taggedSentences.zipWithIndex.flatMap { case (taggedSentence, index) =>
val tagsSentence = taggedSentence.tags.mkString("")
val wordIndexesByMatchedGroups = getWordIndexesByMatchedGroups(tagsSentence)
if (wordIndexesByMatchedGroups.isEmpty) {
taggedSentence.indexedTaggedWords.map(indexedTaggedWord =>
Annotation(
TOKEN,
indexedTaggedWord.begin,
indexedTaggedWord.end,
indexedTaggedWord.word,
Map("sentence" -> index.toString)))
} else {
annotateSegmentWords(wordIndexesByMatchedGroups, taggedSentence, index)
}
}
}
private def getWordIndexesByMatchedGroups(tagsSentence: String): List[List[RegexTagsInfo]] = {
val regexPattern = s"($LEFT_BOUNDARY)($MIDDLE*)*($RIGHT_BOUNDARY)".r
regexPattern
.findAllMatchIn(tagsSentence)
.map(matchedResult => {
val groups = (1 to matchedResult.groupCount).toList
groups
.map(g =>
RegexTagsInfo(
matchedResult.group(g),
matchedResult.start(g),
matchedResult.end(g),
(matchedResult.end(g) / 2) - 1))
.filter(regexTagsInfo => regexTagsInfo.estimatedIndex != -1)
})
.toList
}
private def annotateSegmentWords(
wordIndexesByMatchedGroups: List[List[RegexTagsInfo]],
taggedSentence: TaggedSentence,
sentenceIndex: Int): Seq[Annotation] = {
val singleTaggedWords =
getSingleIndexedTaggedWords(wordIndexesByMatchedGroups, taggedSentence)
val multipleTaggedWords = getMultipleTaggedWords(wordIndexesByMatchedGroups, taggedSentence)
val segmentedTaggedWords = (singleTaggedWords ++ multipleTaggedWords)
.sortWith(
_.metadata.getOrElse("index", "-1").toInt < _.metadata.getOrElse("index", "-1").toInt)
segmentedTaggedWords.map(segmentedTaggedWord =>
Annotation(
TOKEN,
segmentedTaggedWord.begin,
segmentedTaggedWord.end,
segmentedTaggedWord.word,
Map("sentence" -> sentenceIndex.toString)))
}
private def getSingleIndexedTaggedWords(
wordIndexesByMatchedGroups: List[List[RegexTagsInfo]],
taggedSentence: TaggedSentence): List[IndexedTaggedWord] = {
val flattenWordIndexes = wordIndexesByMatchedGroups.flatMap(wordIndexGroup =>
wordIndexGroup.map(wi => wi.estimatedIndex))
val unmatchedTaggedWordsCandidates = taggedSentence.indexedTaggedWords.zipWithIndex
.filter { case (_, index) =>
!flattenWordIndexes.contains(index)
}
.map(_._1)
val unmatchedTaggedWords =
unmatchedTaggedWordsCandidates.filter(unmatchedTaggedWordCandidate =>
!isMatchedWord(unmatchedTaggedWordCandidate, wordIndexesByMatchedGroups))
unmatchedTaggedWords.toList
}
private def isMatchedWord(
indexedTaggedWord: IndexedTaggedWord,
regexTagsInfoList: List[List[RegexTagsInfo]]): Boolean = {
val index = indexedTaggedWord.metadata.getOrElse("index", "-1").toInt
val result = regexTagsInfoList.flatMap(regexTagsInfo => {
val leftBoundaryIndex = regexTagsInfo.head.estimatedIndex
val rightBoundaryIndex = regexTagsInfo.last.estimatedIndex
val isInRange = if (index > leftBoundaryIndex && index < rightBoundaryIndex) true else false
val verifyMatches = regexTagsInfo.map(rti => {
if (indexedTaggedWord.tag != MIDDLE || !isInRange) "unmatched"
else {
if (rti.tagsMatch.contains(MIDDLE) && rti.tagsMatch.length > 2) "matched"
else "unmatched"
}
})
verifyMatches
})
result.contains("matched")
}
private def getMultipleTaggedWords(
wordIndexesByMatchedGroups: List[List[RegexTagsInfo]],
taggedSentence: TaggedSentence): List[IndexedTaggedWord] = {
wordIndexesByMatchedGroups.flatMap { wordIndexesGroup =>
val wordIndexes = wordIndexesGroup.map(wi => wi.estimatedIndex)
val taggedWords = taggedSentence.indexedTaggedWords.zipWithIndex
.filter { case (indexedTaggedWord, index) =>
wordIndexes.contains(index) || isMatchedWord(indexedTaggedWord, List(wordIndexesGroup))
}
.map(_._1)
if (taggedWords.nonEmpty) Some(taggedWords.reduceLeft(processTags)) else None
}
}
private val processTags = (current: IndexedTaggedWord, next: IndexedTaggedWord) => {
val wordSegment = current.word + next.word
val tagSegment = current.tag + next.tag
val begin = if (current.begin <= next.begin) current.begin else next.begin
val end = begin + wordSegment.length - 1
val currentIndexValue = current.metadata.getOrElse("index", "-1")
val nextIndexValue = current.metadata.getOrElse("index", "-1")
val index =
if (currentIndexValue.toInt <= nextIndexValue.toInt) currentIndexValue else nextIndexValue
IndexedTaggedWord(wordSegment, tagSegment, begin, end, None, Map("index" -> index))
}
/** Output Annotator Types: TOKEN
*
* @group anno
*/
override val outputAnnotatorType: AnnotatorType = TOKEN
/** Input Annotator Types: DOCUMENT
*
* @group anno
*/
override val inputAnnotatorTypes: Array[String] = Array(DOCUMENT)
}
private case class RegexTagsInfo(tagsMatch: String, start: Int, end: Int, estimatedIndex: Int)
trait ReadablePretrainedWordSegmenter
extends ParamsAndFeaturesReadable[WordSegmenterModel]
with HasPretrained[WordSegmenterModel] {
override val defaultModelName: Some[String] = Some("wordseg_pku")
override val defaultLang: String = "zh"
/** Java compliant-overrides */
override def pretrained(): WordSegmenterModel = super.pretrained()
override def pretrained(name: String): WordSegmenterModel = super.pretrained(name)
override def pretrained(name: String, lang: String): WordSegmenterModel =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): WordSegmenterModel =
super.pretrained(name, lang, remoteLoc)
}
/** This is the companion object of [[WordSegmenterModel]]. Please refer to that class for the
* documentation.
*/
object WordSegmenterModel extends ReadablePretrainedWordSegmenter
