rs-natural

Natural language processing library written in Rust. Still very much a work in progress. Basically an experiment, but hey maybe something cool will come out of it.

Currently working:

• Jaro-Winkler Distance
• Levenshtein Distance
• Tokenizing
• NGrams (with and without padding)
• Phonetics (Soundex)
• Stemming (Using a fork of rust-stem)
• Naive-Bayes classification

Near-sight goals:

• Logistic regression classification
• Optimize naive-bayes (currently pretty slow)
• Plural/Singular inflector
• tf-idf

How to use

Use at your own risk. Some functionality is missing, some other functionality is slow as molasses because it isn't optomized yet. I'm targeting master, and don't offer backward compatibility.

Setup

It's a crate with a cargo.toml. Add this to your cargo.toml:

[dependencies.natural]
git = "https://github.com/cjqed/rs-natural"

Distance

extern crate natural;
use natural::distance::jaro_winkler_distance;
use natural::distance::levenshtein_distance;

assert_eq!(levenshtein_distance("kitten", "sitting"), 3);
assert_eq!(jaro_winkler_distance("dixon", "dicksonx"), 0.767); 

Note, don't actually assert_eq! on JWD since it returns an f64. To test, I actually use:

fn f64_eq(a: f32, b: f32) {
  assert!((a - b).abs() < 0.01);
}

Phonetics

There are two ways to gain access to the SoundEx algorithm in this library, either through a simple soundex function that accepts two &str parameters and returns a boolean, or through the SoundexWord struct. I will show both here.

use natural::phonetics::soundex;
use natural::phonetics::SoundexWord;

assert!(soundex("rupert", "robert"));


let s1 = SoundexWord::new("rupert");
let s2 = SoundexWord::new("robert");
assert!(s1.sounds_like(s2));
assert!(s1.sounds_like_str("robert"));

Tokenization

extern crate natural;
use natural::tokenize::tokenize;

assert_eq!(tokenize("hello, world!"), vec!["hello", "world"]);
assert_eq!(tokenize("My dog has fleas."), vec!["My", "dog", "has", "fleas"]);

NGrams

You can create an ngram with and without padding, e.g.:

extern crate natural;

use natural::ngram::get_ngram;
use natural::ngram::get_ngram_with_padding;

assert_eq!(get_ngram("hello my darling", 2), vec![vec!["hello", "my"], vec!["my", "darling"]]);

assert_eq!(get_ngram_with_padding("my fleas", 2, "----"), vec![
  vec!["----", "my"], vec!["my", "fleas"], vec!["fleas", "----"]]);

Classification

extern crate natural;
use natural::classifier::NaiveBayesClassifier;

let mut nbc = NaiveBayesClassifier::new();

nbc.train(STRING_TO_TRAIN, LABEL);
nbc.train(STRING_TO_TRAIN, LABEL);
nbc.train(STRING_TO_TRAIN, LABEL);
nbc.train(STRING_TO_TRAIN, LABEL);

nbc.guess(STRING_TO_GUESS); //returns a label with the highest probability