Scala和Spark中文本词形还原的最简单方法

Question

我想在文本文件中使用词形还原:

surprise heard thump opened door small seedy man clasping package wrapped.

upgrading system found review spring 2008 issue moody audio backed.

omg left gotta wrap review order asap . understand hand delivered dali lama

speak hands wear earplugs lives . listen maintain link long .

cables cables finally able hear gem long rumored music .
...

和预期产量是:

surprise heard thump open door small seed man clasp package wrap.

upgrade system found review spring 2008 issue mood audio back.

omg left gotta wrap review order asap . understand hand deliver dali lama

speak hand wear earplug live . listen maintain link long .

cable cable final able hear gem long rumor music .
...

有谁能够帮我 ？谁知道在Scala和Spark中实现的最简单的词形还原方法？

Answer 1

Spark中的"Adavanced analitics"一书中有一个函数,关于词形还原的章节:

  val plainText =  sc.parallelize(List("Sentence to be precessed."))

  val stopWords = Set("stopWord")

  import edu.stanford.nlp.pipeline._
  import edu.stanford.nlp.ling.CoreAnnotations._
  import scala.collection.JavaConversions._

  def plainTextToLemmas(text: String, stopWords: Set[String]): Seq[String] = {
    val props = new Properties()
    props.put("annotators", "tokenize, ssplit, pos, lemma")
    val pipeline = new StanfordCoreNLP(props)
    val doc = new Annotation(text)
    pipeline.annotate(doc)
    val lemmas = new ArrayBuffer[String]()
    val sentences = doc.get(classOf[SentencesAnnotation])
    for (sentence <- sentences; token <- sentence.get(classOf[TokensAnnotation])) {
      val lemma = token.get(classOf[LemmaAnnotation])
      if (lemma.length > 2 && !stopWords.contains(lemma)) {
        lemmas += lemma.toLowerCase
      }
    }
    lemmas
  }

  val lemmatized = plainText.map(plainTextToLemmas(_, stopWords))
  lemmatized.foreach(println)

现在只需将它用于mapper中的每一行.

val lemmatized = plainText.map(plainTextToLemmas(_, stopWords))

编辑:

我添加到代码行

import scala.collection.JavaConversions._

这是必要的,因为否则句子是Java而不是Scala List.这应该现在编译没有问题.

我使用了scala 2.10.4和fallowing stanford.nlp依赖项:

<dependency>
  <groupId>edu.stanford.nlp</groupId>
  <artifactId>stanford-corenlp</artifactId>
  <version>3.5.2</version>
</dependency>
<dependency>
  <groupId>edu.stanford.nlp</groupId>
  <artifactId>stanford-corenlp</artifactId>
  <version>3.5.2</version>
  <classifier>models</classifier>
</dependency>

您还可以查看stanford.nlp页面中有很多示例(在Java中)http://nlp.stanford.edu/software/corenlp.shtml.

编辑:

MapPartition版本:

虽然我不知道它是否会显着加快工作.

  def plainTextToLemmas(text: String, stopWords: Set[String], pipeline: StanfordCoreNLP): Seq[String] = {
    val doc = new Annotation(text)
    pipeline.annotate(doc)
    val lemmas = new ArrayBuffer[String]()
    val sentences = doc.get(classOf[SentencesAnnotation])
    for (sentence <- sentences; token <- sentence.get(classOf[TokensAnnotation])) {
      val lemma = token.get(classOf[LemmaAnnotation])
      if (lemma.length > 2 && !stopWords.contains(lemma)) {
        lemmas += lemma.toLowerCase
      }
    }
    lemmas
  }

  val lemmatized = plainText.mapPartitions(p => {
    val props = new Properties()
    props.put("annotators", "tokenize, ssplit, pos, lemma")
    val pipeline = new StanfordCoreNLP(props)
    p.map(q => plainTextToLemmas(q, stopWords, pipeline))
  })
  lemmatized.foreach(println)