Punkt

Implementation of Tibor Kiss' and Jan Strunk's Punkt algorithm for sentence tokenization. Results have been compared with small and large texts that have been tokenized using NLTK.

Usage

For full examples, see rust-punkt/examples

The punkt algorithm allows you to derive all the necessary data to perform sentence tokenization from the document itself.

let doc = "I bought $5.50 worth of apples from the store. I gave them to my dog when I came home.";
let trainer: Trainer<Standard> = Trainer::new();
let mut data = TrainingData::new();

trainer.train(doc, &mut data);

for s in SentenceTokenizer::<Standard>::new(doc, &data) {
  println!("{:?}", s);
}

rust-punkt also provides pretrained data that can be loaded for certain languages.

let data = TrainingData::english();
...

rust-punkt also allows training data to be incrementally gathered.

let trainer: Trainer<Standard> = Trainer::new();
let mut data = TrainingData::new();

for d in docs.iter() {
  trainer.train(d, &mut data);

  for s in SentenceTokenizer::<Standard>::new(d, &data) {
    println!("{:?}", s);
  }
}

Customization

For a full example, see rust-punkt/examples/custom-parameters.rs

rust-punkt exposes a number of traits to customize how the trainer, sentence tokenizer, and internal tokenizers work. The default settings, which are nearly identical, to the ones available in the Python library are available in punkt::params::Standard.

To modify only how the trainer works:

struct MyParams;

impl DefaultCharacterDefinitions for MyParams { }

impl TrainerParameters for MyParams {
  ...
}

To fully modify how everything works:

struct MyParams;

impl DefinesSentenceEndings for MyParams { 
  ...
}

impl DefinesInternalPunctuation for MyParams {
  ...
}

impl DefinesNonWordCharacters for MyParams { 
  ...
}

impl DefinesPunctuation for MyParams {
  ...
}

impl DefinesNonPrefixCharacters for MyParams {
  ...
}

impl TrainerParameters for MyParams {
  ...
}

Benchmarks

Specs of my machine:

• i5-4460 @ 3.20 x 4
• 8 GB RAM
• Fedora 20
• SSD

test tokenizer::bench_sentence_tokenizer_train_on_document_long   ... bench: 129,877,668 ns/iter (+/- 6,935,294)
test tokenizer::bench_sentence_tokenizer_train_on_document_medium ... bench:     901,867 ns/iter (+/- 12,984)
test tokenizer::bench_sentence_tokenizer_train_on_document_short  ... bench:     702,976 ns/iter (+/- 13,554)
test tokenizer::word_tokenizer_bench_long                         ... bench:  14,897,528 ns/iter (+/- 689,138)
test tokenizer::word_tokenizer_bench_medium                       ... bench:     339,535 ns/iter (+/- 21,692)
test tokenizer::word_tokenizer_bench_short                        ... bench:     281,293 ns/iter (+/- 3,256)
test tokenizer::word_tokenizer_bench_very_long                    ... bench:  54,256,241 ns/iter (+/- 1,210,575)
test trainer::bench_trainer_long                                  ... bench:  27,674,731 ns/iter (+/- 550,338)
test trainer::bench_trainer_medium                                ... bench:     681,222 ns/iter (+/- 31,713)
test trainer::bench_trainer_short                                 ... bench:     527,203 ns/iter (+/- 11,354)
test trainer::bench_trainer_very_long                             ... bench:  98,221,585 ns/iter (+/- 5,297,733)

Python results for sentence tokenization, and training on the document (the first 3 tests mirrored from above):

The following script was used to benchmark NLTK.

• f0 is the contents of the file that is being tokenized.
• s is an instance of a PunktSentenceTokenizer.
• timed is the total time it takes to run tests number of tests.

False is being passed into tokenize to prevent NLTK from aligning sentence boundaries. This functionality is currently unimplemented.

timed = timeit.timeit('s.train(f0); [s for s in s.tokenize(f0, False)]', 'from bench import s, f0', number=tests)
print(timed)
print(timed / tests)

long    - 1.3414202709775418 s   = 1.34142 x 10^9 ns ~ 10.3283365927x improvement 
medium  - 0.007250561956316233 s = 7.25056 x 10^6 ns ~ 8.03950245027x improvement
short   - 0.005532620595768094 s = 5.53262 x 10^6 ns ~ 7.870283759x   improvement

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.