ngram: Machine Learning Feature Extractors

Description

Feature Extractors that can be used with mtText.

Usage

  ngramCount(ngramLength = 1, skipLength = 0, maxNumTerms = 1e+07,
    weighting = "tf")

  ngramHash(ngramLength = 1, skipLength = 0, hashBits = 16,
    seed = 314489979, ordered = TRUE, invertHash = 0)

Arguments

ngramLength

An integer that specifies the maximum number of tokens to take when constructing an n-gram. The default value is 1.

skipLength

An integer that specifies the maximum number of tokens to skip when constructing an n-gram. If the value specified as skip length is k, then n-grams can contain up to k skips (not necessarily consecutive). For example, if k=2, then the 3-grams extracted from the text "the sky is blue today" are: "the sky is", "the sky blue", "the sky today", "the is blue", "the is today" and "the blue today". The default value is 0.

maxNumTerms

An integer that specifies the maximum number of categories to include in the dictionary. The default value is 10000000.

weighting

A character string that specifies the weighting criteria:

  • "tf": to use term frequency.
  • "idf": to use inverse document frequency.
  • "tfidf": to use both term frequency and inverse document frequency.

hashBits

integer value. Number of bits to hash into. Must be between 1 and 30, inclusive.

seed

integer value. Hashing seed.

ordered

TRUE to include the position of each term in the hash. Otherwise, FALSE. The default value is TRUE.

invertHash

An integer specifying the limit on the number of keys that can be used to generate the slot name. 0 means no invert hashing; -1 means no limit. While a zero value gives better performance, a non-zero value is needed to get meaningful coefficent names.

Details

ngramCount allows defining arguments for count-based feature extraction. It accepts following options: ngramLength, skipLength, maxNumTerms and weighting.

ngramHash allows defining arguments for hashing-based feature extraction. It accepts the following options: ngramLength, skipLength, hashBits, seed, ordered and invertHash.

Value

A character string defining the transform.

Author(s)

Microsoft Corporation Microsoft Technical Support

See Also

featurizeText.

Examples


  myData <- data.frame(opinion = c(
     "I love it!",
     "I love it!",
     "Love it!",
     "I love it a lot!",
     "Really love it!",
     "I hate it",
     "I hate it",
     "I hate it.",
     "Hate it",
     "Hate"),
     like = rep(c(TRUE, FALSE), each = 5),
     stringsAsFactors = FALSE)

 outModel1 <- rxLogisticRegression(like~opinionCount, data = myData, 
     mlTransforms = list(featurizeText(vars = c(opinionCount = "opinion"), 
         wordFeatureExtractor = ngramHash(invertHash = -1, hashBits = 3)))) 
 summary(outModel1)   

 outModel2 <- rxLogisticRegression(like~opinionCount, data = myData, 
     mlTransforms = list(featurizeText(vars = c(opinionCount = "opinion"), 
         wordFeatureExtractor = ngramCount(maxNumTerms = 5, weighting = "tf"))))         
 summary(outModel2)