nickto / wordtokenizers.jl

Tokenizers for NLP

WordTokenizers

Some basic tokenizers for Natural Language Processing:

The normal way to used this package is to call tokenize(str) to split up a string into words; or split_sentences(str) to split up a string into sentences. Maybe even tokenize.(split_sentences(str)) to do both.

tokenize and split_sentences, are configurable functions that call one of the tokenizers or sentence splitters defined below. They have sensible defaults set, but you can override the method used by calling set_tokenizer(func) or set_sentence_splitter(func) passing in your preferred function func from the list below (or from else where) Configuring them this way will throw up a method overwritten warning, and trigger recompilation of any methods that use them.

This means if you are using a package that uses WordTokenizers.jl to do tokenization/sentence splitting via the default methods; changing the tokenizer/splitter will change the behavior of that package. This is a feature of CorpusLoaders.jl. If as a package author you don't want to allow the user to change the tokenizer in this way, you should use the tokenizer you want explicitly, rather than using the tokenize method.

Example Setting Tokenizer (Revtok.jl)

You might like to, for example use Revtok.jl's tokenizer. We do not include Revtok in this package, because making use of it with-in WordTokenizers.jl is trival. Just import Revtok; set_tokenizer(Revtok.tokenize).

Full example:

julia> using WordTokenizers

julia> text = "I cannot stand when they say \"Enough is enough.\"";

julia> tokenize(text) |> print # Default tokenizer
SubString{String}["I", "can", "not", "stand", "when", "they", "say", "``", "Enough", "is", "enough", ".", "''"]

julia> import Revtok

julia> set_tokenizer(Revtok.tokenize)
WARNING: Method definition tokenize(AbstractString) in module WordTokenizers overwritten
tokenize (generic function with 1 method)


julia> tokenize(text) |> print # Revtok's tokenizer
String[" I ", " cannot ", " stand ", " when ", " they ", " say ", " \"", " Enough ", " is ", " enough ", ".\" "]

(Word) Tokenizers

The word tokenizers basically assume sentence splitting has already been done.

• Poorman's tokenizer: (poormans_tokenize) Deletes all punctuation, and splits on spaces. (In some ways worse than just using split)

• Punctuation space tokenize: (punctuation_space_tokenize) Marginally improved version of the poorman's tokenizer, only deletes punctuation occurring outside words.

• Penn Tokenizer: (penn_tokenize) This is Robert MacIntyre's orginal tokenizer used for the Penn Treebank. Splits contractions.

• Improved Penn Tokenizer: (improved_penn_tokenize) NLTK's improved Penn Treebank Tokenizer. Very similar to the original, some improvements on punctuation and contractions. This matches to NLTK's nltk.tokenize.TreeBankWordTokenizer.tokenize

• NLTK Word tokenizer: (nltk_word_tokenize) NLTK's even more improved version of the Penn Tokenizer. This version has better unicode handling and some other changes. This matches to the most commonly used nltk.word_tokenize, minus the sentence tokenizing step.

• Reversible Tokenizer: (rev_tokenize and rev_detokenize) This tokenizer splits on punctuations, space and special symbols. The generated tokens can be de-tokenized by using the rev_detokenizer function into the state before tokenization.

• TokTok Tokenizer: (toktok_tokenize) This tokenizer is a simple, general tokenizer, where the input has one sentence per line; thus only final period is tokenized. Tok-tok has been tested on and gives reasonably good results for English, Persian, Russian, Czech, French, German, Vietnamese, Tajik, and a few others. (default tokenizer)

  (To me it seems like a weird historical thing that NLTK has 2 successive variation on improving the Penn tokenizer, but for now I am matching it and having both. See [NLTK#2005])

Sentence Splitters

We currently only have one sentence splitter.

• Rule Based Sentence Spitter: (rulebased_split_sentences), uses a rule that periods, question marks, and exclamation marks, followed by white-space end sentences. With a large list of exceptions.

split_sentences is exported as an alias for the most useful sentence splitter currently implemented. (Which ATM is the only sentence splitter: rulebased_split_sentences) (default sentence_splitter)

Example

julia> tokenize("The package's tokenizers range from simple (e.g. poorman's), to complex (e.g. Penn).") |> print
SubString{String}["The", "package", "'s", "tokenizers", "range", "from", "simple", "(", "e.g.", "poorman", "'s", ")",",", "to", "complex", "(", "e.g.", "Penn", ")", "."]

julia> text = "The leatherback sea turtle is the largest, measuring six or seven feet (2 m) in length at maturity, and three to five feet (1 to 1.5 m) in width, weighing up to 2000 pounds (about 900 kg). Most other species are smaller, being two to four feet in length (0.5 to 1 m) and proportionally less wide. The Flatback turtle is found solely on the northerncoast of Australia.";

julia> split_sentences(text)
3-element Array{SubString{String},1}:
 "The leatherback sea turtle is the largest, measuring six or seven feet (2 m) in length at maturity, and three to five feet (1 to 1.5 m) in width, weighing up to 2000 pounds (about900 kg). "
 "Most other species are smaller, being two to four feet in length (0.5 to 1 m) and proportionally less wide. "
 "The Flatback turtle is found solely on the northern coast of Australia."

julia> tokenize.(split_sentences(text))
3-element Array{Array{SubString{String},1},1}:
 SubString{String}["The", "leatherback", "sea", "turtle", "is", "the", "largest", ",", "measuring", "six"  …  "up", "to", "2000", "pounds", "(", "about", "900", "kg", ")", "."]
 SubString{String}["Most", "other", "species", "are", "smaller", ",", "being", "two", "to", "four"  …  "0.5", "to", "1", "m", ")", "and", "proportionally", "less", "wide", "."]
 SubString{String}["The", "Flatback", "turtle", "is", "found", "solely", "on", "the", "northern", "coast", "of", "Australia", "."]

Experimental API

I am trying out an experimental API where these are added as dispatches to Base.split

So
split(foo, Words) is the same as tokenize(foo),
and
split(foo, Sentences) is the same as split_sentences(foo).