Cadmium::Tokenizer (wip)

This module contains several string tokenizers. Each one has its use cases and some are faster (or way slower) than others.

Installation

  1. Add the dependency to your shard.yml:

    dependencies:
  cadmium_tokenizer:
    github: cadmiumcr/tokenizer

  2. Run shards install

Usage

require "cadmium_tokenizer"

Aggressive Tokenizer

The aggressive tokenizer currently has localization available for:

If no language is included it will default to English.

Use it like so:

tokenizer = Cadmium.aggressive_tokenizer.new(lang: :es)
tokenizer.tokenize("hola yo me llamo eduardo y esudié ingeniería")
# => ["hola", "yo", "me", "llamo", "eduardo", "y", "esudié", "ingeniería"]

Case Tokenizer

The case tokenizer doesn't rely on Regex and as such should be pretty fast. It should also work on an international basis fairly easily.

tokenizer = Cadmium.case_tokenizer.new
tokenizer.tokenize("these are strings")
# => ["these", "are", "strings"]

tokenizer = Cadmium.case_tokenizer.new(preserve_apostrophes: true)
tokenizer.tokenize("Affectueusement surnommé « Gabo » dans toute l'Amérique latine")
# => ["Affectueusement", "surnommé", "Gabo", "dans", "toute", "l", "Amérique", "latine"]

Regex Tokenizer

The whitespace tokenizer, word punctuation tokenizer, and word tokenizer all extend the regex tokenizer. It uses Regex to match on the correct values.

tokenizer = Cadmium.word_punctuation_tokenizer.new
tokenizer.tokenize("my dog hasn't any fleas.")
# => ["my", "dog", "hasn", "'", "t", "any", "fleas", "."]

Treebank Word Tokenizer

The treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank. This implementation is a port of the tokenizer sed script written by Robert McIntyre. To read about treebanks you can visit wikipedia.

tokenizer = Cadmium.treebank_word_tokenizer.new
tokenizer.tokenize("If we 'all' can't go. I'll stay home.")
# => ["If", "we", "'all", "'", "ca", "n't", "go.", "I", "'ll", "stay", "home", "."]

Pragmatic Tokenizer

The pragmatic tokenizer is based off of the ruby gem from diasks2 which you can find here. It is a multilengual tokenizer which provides a wide array of options for tokenizing strings. For complete documentation check here.

Example is taken directly from the diasks2/pragmatic_tokenizer documentation, with a few modifications. Currently supported languages are:

text = "\"I said, 'what're you? Crazy?'\" said Sandowsky. \"I can't afford to do that.\""

Cadmium.pragmatic_tokenizer.new.tokenize(text)
# => ["\"", "i", "said", ",", "'", "what're", "you", "?", "crazy", "?", "'", "\"", "said", "sandowsky", ".", "\"", "i", "can't", "afford", "to", "do", "that", ".", "\""]

The initializer accepts the following options:

language:            :en, # the language of the string you are tokenizing
abbreviations:       Set{"a.b", "a"}, # a user-supplied array of abbreviations (downcased with ending period removed)
stop_words:          Set{"is", "the"}, # a user-supplied array of stop words (downcased)
remove_stop_words:   true, # remove stop words
contractions:        { "i'm" => "i am" }, # a user-supplied hash of contractions (key is the contracted form; value is the expanded                                             form - both the key and value should be downcased)
expand_contractions: true, # (i.e. ["isn't"] will change to two tokens ["is", "not"])
filter_languages:    [:en, :de], # process abbreviations, contractions and stop words for this array of languages
punctuation:         :none, # see below for more details
numbers:             :none, # see below for more details
remove_emoji:        true, # remove any emoji tokens
remove_urls:         true, # remove any urls
remove_emails:       true, # remove any emails
remove_domains:      true, # remove any domains
hashtags:            :keep_and_clean, # remove the hastag prefix
mentions:            :keep_and_clean, # remove the @ prefix
clean:               true, # remove some special characters
classic_filter:      true, # removes dots from acronyms and 's from the end of tokens
downcase:            false, # do not downcase tokens
minimum_length:      3, # remove any tokens less than 3 characters
long_word_split:     10 # split tokens longer than 10 characters at hypens or underscores

Contributing

  1. Fork it (https://github.com/cadmiumcr/cadmium_tokenizer/fork)
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create a new Pull Request

Contributors