stopwordsDefault: Machine Learning Text Transform

Description

Text transforms that can be performed on data before training a model.

Usage

  stopwordsDefault()

  stopwordsCustom(dataFile = "")

  termDictionary(terms = "", dataFile = "", sort = "occurrence")

  featurizeText(vars, language = "English", stopwordsRemover = NULL,
    case = "lower", keepDiacritics = FALSE, keepPunctuations = TRUE,
    keepNumbers = TRUE, dictionary = NULL,
    wordFeatureExtractor = ngramCount(), charFeatureExtractor = NULL,
    vectorNormalizer = "l2", ...)

Arguments

dataFile

character: . Data file containing the terms (short form data).

terms

An optional character vector of terms or categories.

sort

Specifies how to order items when vectorized. Two orderings are supported:

  • "occurrence": items appear in the order encountered.
  • "value": items are sorted according to their default comparison. For example, text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').

vars

A named list of character vectors of input variable names and the name of the output variable. Note that the input variables must be of the same type. For one-to-one mappings between input and output variables, a named character vector can be used.

language

Secifies the language used in the data set. The following values are supported:

  • "AutoDetect": for automatic language detection.
  • "English".
  • "French".
  • "German".
  • "Dutch".
  • "Italian".
  • "Spanish".
  • "Japanese".

stopwordsRemover

Specifies the stopwords remover to use. There are three options supported:

  • NULL No stopwords remover is used.
  • stopwordsDefault: A precompiled language-specific lists of stop words is used that includes the most common words from Microsoft Office.
  • stopwordsCustom: A user-defined list of stopwords. It accepts the following option: dataFile.
    The default value is NULL.

case

Text casing using the rules of the invariant culture. Takes the following values:

  • "lower".
  • "upper".
  • "none".
    The default value is "lower".

keepDiacritics

FALSE to remove diacritical marks; TRUE to retain diacritical marks. The default value is FALSE.

keepPunctuations

FALSE to remove punctuation; TRUE to retain punctuation. The default value is TRUE.

keepNumbers

FALSE to remove numbers; TRUE to retain numbers. The default value is TRUE.

dictionary

A termDictionary of whitelisted terms which accepts the following options:

  • terms,
  • dataFile, and
  • sort.
    The default value is NULL. Note that the stopwords list takes precedence over the dictionary whitelist as the stopwords are removed before the dictionary terms are whitelisted.

wordFeatureExtractor

Specifies the word feature extraction arguments. There are two different feature extraction mechanisms:

  • ngramCount: Count-based feature extraction (equivalent to WordBag). It accepts the following options: maxNumTerms and weighting.
  • ngramHash: Hashing-based feature extraction (equivalent to WordHashBag). It accepts the following options: hashBits, seed, ordered and invertHash.
    The default value is ngramCount.

charFeatureExtractor

Specifies the char feature extraction arguments. There are two different feature extraction mechanisms:

  • ngramCount: Count-based feature extraction (equivalent to WordBag). It accepts the following options: maxNumTerms and weighting.
  • ngramHash: Hashing-based feature extraction (equivalent to WordHashBag). It accepts the following options: hashBits, seed, ordered and invertHash.
    The default value is NULL.

vectorNormalizer

Normalize vectors (rows) individually by rescaling them to unit norm. Takes one of the following values:

  • "none".
  • "l2".
  • "l1".
  • "linf". The default value is "l2".

...

Additional arguments sent to the compute engine.

Details

The featurizeText transform produces a bag of counts of
sequences of consecutive words, called n-grams, from a given corpus of text. There are two ways it can do this:

  • build a dictionary of n-grams and use the id in the dictionary as the index in the bag;

  • hash each n-gram and use the hash value as the index in the bag.

The purpose of hashing is to convert variable-length text documents into equal-length numeric feature vectors, to support dimensionality reduction and to make the lookup of feature weights faster.

The text transform is applied to text input columns. It offers language detection, tokenization, stopwords removing, text normalization and feature generation. It supports the following languages by default: English, French, German, Dutch, Italian, Spanish and Japanese.

The n-grams are represented as count vectors, with vector slots corresponding either to n-grams (created using ngramCount) or to their hashes (created using ngramHash). Embedding ngrams in a vector space allows their contents to be compared in an efficient manner. The slot values in the vector can be weighted by the following factors:

  • term frequency - The number of occurrences of the slot in the text

  • inverse document frequency - A ratio (the logarithm of inverse relative slot frequency) that measures the information a slot provides by determining how common or rare it is across the entire text.

  • term frequency-inverse document frequency - the product term frequency and the inverse document frequency.

Value

A maml object defining the transform.

Author(s)

Microsoft Corporation Microsoft Technical Support

See Also

ngramCount, ngramHash, rxFastTrees, rxFastForest, rxNeuralNet, rxOneClassSvm, rxLogisticRegression.

Examples


 trainReviews <- data.frame(review = c( 
         "This is great",
         "I hate it",
         "Love it",
         "Do not like it",
         "Really like it",
         "I hate it",
         "I like it a lot",
         "I kind of hate it",
         "I do like it",
         "I really hate it",
         "It is very good",
         "I hate it a bunch",
         "I love it a bunch",
         "I hate it",
         "I like it very much",
         "I hate it very much.",
         "I really do love it",
         "I really do hate it",
         "Love it!",
         "Hate it!",
         "I love it",
         "I hate it",
         "I love it",
         "I hate it",
         "I love it"),
      like = c(TRUE, FALSE, TRUE, FALSE, TRUE,
         FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE,
         FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, 
         FALSE, TRUE, FALSE, TRUE), stringsAsFactors = FALSE
     )

     testReviews <- data.frame(review = c(
         "This is great",
         "I hate it",
         "Love it",
         "Really like it",
         "I hate it",
         "I like it a lot",
         "I love it",
         "I do like it",
         "I really hate it",
         "I love it"), stringsAsFactors = FALSE)


 outModel <- rxLogisticRegression(like ~ reviewTran, data = trainReviews,
     mlTransforms = list(featurizeText(vars = c(reviewTran = "review"),
     stopwordsRemover = stopwordsDefault(), keepPunctuations = FALSE)))
 # 'hate' and 'love' have non-zero weights
 summary(outModel)

 # Use the model to score
 scoreOutDF5 <- rxPredict(outModel, data = testReviews, 
     extraVarsToWrite = "review")
 scoreOutDF5