scala - 如何使用 JohnSnowLabs NLP 拼写校正模块 NorvigSweetingModel？

Question

我正在浏览 JohnSnowLabs SpellChecker here .

我在那里找到了Norvig的算法实现，示例部分只有以下两行:

import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
NorvigSweetingModel.pretrained()

我如何在下面的数据框 (df) 上应用这个预训练模型来拼写更正“names”列？

+----------------+---+------------+
|           names|age|       color|
+----------------+---+------------+
|      [abc, cde]| 19|    red, abc|
|[eefg, efa, efb]|192|efg, efz efz|
+----------------+---+------------+

我试过如下:

val schk = NorvigSweetingModel.pretrained().setInputCols("names").setOutputCol("Corrected")

val cdf = schk.transform(df)

但是上面的代码给了我以下错误:

java.lang.IllegalArgumentException: requirement failed: Wrong or missing inputCols annotators in SPELL_a1f11bacb851. Received inputCols: names. Make sure such columns have following annotator types: token
  at scala.Predef$.require(Predef.scala:224)
  at com.johnsnowlabs.nlp.AnnotatorModel.transform(AnnotatorModel.scala:51)
  ... 49 elided

Answer 1

spark-nlp旨在用于其自己的特定管道，不同转换器的输入列必须包含特殊的元数据。

异常已经告诉您 NorvigSweetingModel 的输入应该标记化:

Make sure such columns have following annotator types: token

如果我没记错的话，至少你会在这里组装文档并标记化。

import com.johnsnowlabs.nlp.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
import com.johnsnowlabs.nlp.annotators.Tokenizer
import org.apache.spark.ml.Pipeline

val df = Seq(Seq("abc", "cde"), Seq("eefg", "efa", "efb")).toDF("names")

val nlpPipeline = new Pipeline().setStages(Array(
  new DocumentAssembler().setInputCol("names").setOutputCol("document"),
  new Tokenizer().setInputCols("document").setOutputCol("tokens"),
  NorvigSweetingModel.pretrained().setInputCols("tokens").setOutputCol("corrected")
))

A Pipeline像这样，可以通过小的调整应用于您的数据 - 输入数据必须是 string不是 array<string> *:

val result = df
  .transform(_.withColumn("names", concat_ws(" ", $"names")))
  .transform(df => nlpPipeline.fit(df).transform(df))
result.show()

+------------+--------------------+--------------------+--------------------+
|       names|            document|              tokens|           corrected|
+------------+--------------------+--------------------+--------------------+
|     abc cde|[[document, 0, 6,...|[[token, 0, 2, ab...|[[token, 0, 2, ab...|
|eefg efa efb|[[document, 0, 11...|[[token, 0, 3, ee...|[[token, 0, 3, ee...|
+------------+--------------------+--------------------+--------------------+

如果你想要一个可以导出的输出，你应该扩展你的 Pipeline与 Finisher .

import com.johnsnowlabs.nlp.Finisher

new Finisher().setInputCols("corrected").transform(result).show

 +------------+------------------+
 |       names|finished_corrected|
 +------------+------------------+
 |     abc cde|        [abc, cde]|
 |eefg efa efb|  [eefg, efa, efb]|
 +------------+------------------+

---

* 根据 the docs DocumentAssembler

can read either a String column or an Array[String]

但在 1.7.3 中它看起来不像在实践中起作用:

df.transform(df => nlpPipeline.fit(df).transform(df)).show()

org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(names)' due to data type mismatch: argument 1 requires string type, however, '`names`' is of array<string> type.;;
'Project [names#62, UDF(names#62) AS document#343]
+- AnalysisBarrier
      +- Project [value#60 AS names#62]
         +- LocalRelation [value#60]