ApacheSpark中的高效字符串匹配 [英] Efficient string matching in Apache Spark

问题描述

我使用OCR工具从截图中提取文本(每个截图大约1-5句)。但是，在手动验证提取的文本时，我注意到不时会出现几个错误。

考虑到文字"你好星火！我真的很喜欢😊❤️！"，我注意到：

1)字母"i"、"！"和"l"被替换为"|"。

2)表情符号未正确提取并被其他字符替换或被省略。

3)不时删除空格。

结果，我可能会得到这样的字符串："Hello here 7l|Real|y Like Spark！"

由于我正在尝试将这些字符串与包含正确文本的数据集(在本例中为"Hello Here😊！我真的很喜欢Spark❤️！")进行匹配，因此我正在寻找一种有效的方法来匹配Spark中的字符串。

谁能为Spark提出一个有效的算法，让我将摘录的文本(~100.000)与我的数据集(~1亿)进行比较？

推荐答案

我从一开始就不会使用Spark，但如果您真的致力于特定的堆栈，您可以组合一组ML转换器以获得最佳匹配。您需要Tokenizer(或split)：

import org.apache.spark.ml.feature.RegexTokenizer

val tokenizer = new RegexTokenizer().setPattern("").setInputCol("text").setMinTokenLength(1).setOutputCol("tokens")

NGram(例如3-gram)

import org.apache.spark.ml.feature.NGram

val ngram = new NGram().setN(3).setInputCol("tokens").setOutputCol("ngrams")

Vectorizer(例如CountVectorizer或HashingTF)：

import org.apache.spark.ml.feature.HashingTF

val vectorizer = new HashingTF().setInputCol("ngrams").setOutputCol("vectors")

和LSH：

import org.apache.spark.ml.feature.{MinHashLSH, MinHashLSHModel}

// Increase numHashTables in practice.
val lsh = new MinHashLSH().setInputCol("vectors").setOutputCol("lsh")

与Pipeline

组合

import org.apache.spark.ml.Pipeline

val pipeline = new Pipeline().setStages(Array(tokenizer, ngram, vectorizer, lsh))

适合示例数据：

val query = Seq("Hello there 7l | real|y like Spark!").toDF("text")
val db = Seq(
  "Hello there 😊! I really like Spark ❤️!", 
  "Can anyone suggest an efficient algorithm"
).toDF("text")

val model = pipeline.fit(db)

同时转换：

val dbHashed = model.transform(db)
val queryHashed = model.transform(query)

和联接

model.stages.last.asInstanceOf[MinHashLSHModel]
  .approxSimilarityJoin(dbHashed, queryHashed, 0.75).show

+--------------------+--------------------+------------------+                  
|            datasetA|            datasetB|           distCol|
+--------------------+--------------------+------------------+
|[Hello there 😊! ...|[Hello there 7l |...|0.5106382978723405|
+--------------------+--------------------+------------------+

同样的方法也可以在Pyspark中使用

from pyspark.ml import Pipeline
from pyspark.ml.feature import RegexTokenizer, NGram, HashingTF, MinHashLSH

query = spark.createDataFrame(
    ["Hello there 7l | real|y like Spark!"], "string"
).toDF("text")

db = spark.createDataFrame([
    "Hello there 😊! I really like Spark ❤️!", 
    "Can anyone suggest an efficient algorithm"
], "string").toDF("text")


model = Pipeline(stages=[
    RegexTokenizer(
        pattern="", inputCol="text", outputCol="tokens", minTokenLength=1
    ),
    NGram(n=3, inputCol="tokens", outputCol="ngrams"),
    HashingTF(inputCol="ngrams", outputCol="vectors"),
    MinHashLSH(inputCol="vectors", outputCol="lsh")
]).fit(db)

db_hashed = model.transform(db)
query_hashed = model.transform(query)

model.stages[-1].approxSimilarityJoin(db_hashed, query_hashed, 0.75).show()
# +--------------------+--------------------+------------------+
# |            datasetA|            datasetB|           distCol|
# +--------------------+--------------------+------------------+
# |[Hello there 😊! ...|[Hello there 7l |...|0.5106382978723405|
# +--------------------+--------------------+------------------+

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