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/* * Copyright © 2020 Peter M. Stahl pemistahl@gmail.com * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either expressed or implied. * See the License for the specific language governing permissions and * limitations under the License. */ //! ## 1. What does this library do? //! //! Its task is simple: It tells you which language some provided textual data is written in. //! This is very useful as a preprocessing step for linguistic data in natural language //! processing applications such as text classification and spell checking. //! Other use cases, for instance, might include routing e-mails to the right geographically //! located customer service department, based on the e-mails' languages. //! //! ## 2. Why does this library exist? //! //! Language detection is often done as part of large machine learning frameworks or natural //! language processing applications. In cases where you don't need the full-fledged //! functionality of those systems or don't want to learn the ropes of those, //! a small flexible library comes in handy. //! //! So far, the only other comprehensive open source library in the Rust ecosystem for //! this task is [*Whatlang*](https://github.com/greyblake/whatlang-rs). //! Unfortunately, it has two major drawbacks: //! //! 1. Detection only works with quite lengthy text fragments. For very short text snippets //! such as Twitter messages, it does not provide adequate results. //! 2. The more languages take part in the decision process, the less accurate are the //! detection results. //! //! *Lingua* aims at eliminating these problems. It nearly does not need any configuration and //! yields pretty accurate results on both long and short text, even on single words and phrases. //! It draws on both rule-based and statistical methods but does not use any dictionaries of words. //! It does not need a connection to any external API or service either. //! Once the library has been downloaded, it can be used completely offline. //! //! ## 3. Which languages are supported? //! //! Compared to other language detection libraries, *Lingua's* focus is on *quality over quantity*, //! that is, getting detection right for a small set of languages first before adding new ones. //! Currently, 74 languages are supported. They are listed as variants in the //! [`Language`](./enum.Language.html) enum. //! //! ## 4. How good is it? //! //! *Lingua* is able to report accuracy statistics for some bundled test data available for each //! supported language. The test data for each language is split into three parts: //! //! 1. a list of single words with a minimum length of 5 characters //! 2. a list of word pairs with a minimum length of 10 characters //! 3. a list of complete grammatical sentences of various lengths //! //! Both the language models and the test data have been created from separate documents of the //! [Wortschatz corpora](https://wortschatz.uni-leipzig.de) offered by Leipzig University, Germany. //! Data crawled from various news websites have been used for training, each corpus comprising one //! million sentences. For testing, corpora made of arbitrarily chosen websites have been used, //! each comprising ten thousand sentences. From each test corpus, a random unsorted subset of //! 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively. //! //! Given the generated test data, I have compared the detection results of *Lingua* and *Whatlang* //! running over the data of *Lingua's* supported 74 languages. Languages that are not supported //! by *Whatlang* are simply ignored for this library during the detection process. //! //! The [bar and box plots](https://github.com/pemistahl/lingua-rs/blob/master/ACCURACY_PLOTS.md) //! show the measured accuracy values for all three performed tasks: Single word detection, //! word pair detection and sentence detection. *Lingua* clearly outperforms its contender. //! Detailed statistics including mean, median and standard deviation values for each language //! and classifier are available in //! [tabular form](https://github.com/pemistahl/lingua-rs/blob/master/ACCURACY_TABLE.md) as well. //! //! ## 5. Why is it better than other libraries? //! //! Every language detector uses a probabilistic [n-gram](https://en.wikipedia.org/wiki/N-gram) //! model trained on the character distribution in some training corpus. Most libraries only use //! n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text //! fragments consisting of multiple sentences. For short phrases or single words, however, //! trigrams are not enough. The shorter the input text is, the less n-grams are available. //! The probabilities estimated from such few n-grams are not reliable. This is why *Lingua* makes //! use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct //! language. //! //! A second important difference is that *Lingua* does not only use such a statistical model, but //! also a rule-based engine. This engine first determines the alphabet of the input text and //! searches for characters which are unique in one or more languages. If exactly one language can //! be reliably chosen this way, the statistical model is not necessary anymore. In any case, the //! rule-based engine filters out languages that do not satisfy the conditions of the input text. //! Only then, in a second step, the probabilistic n-gram model is taken into consideration. //! This makes sense because loading less language models means less memory consumption and better //! runtime performance. //! //! In general, it is always a good idea to restrict the set of languages to be considered in the //! classification process using the respective api methods. If you know beforehand that certain //! languages are never to occur in an input text, do not let those take part in the classifcation //! process. The filtering mechanism of the rule-based engine is quite good, however, filtering //! based on your own knowledge of the input text is always preferable. //! //! ## 6. How to use? //! //! ### 6.1 Basic usage //! //! ``` //! use lingua::{Language, LanguageDetector, LanguageDetectorBuilder}; //! use lingua::Language::{English, French, German, Spanish}; //! //! let languages = vec![English, French, German, Spanish]; //! let detector: LanguageDetector = LanguageDetectorBuilder::from_languages(&languages).build(); //! let detected_language: Option<Language> = detector.detect_language_of("languages are awesome"); //! //! assert_eq!(detected_language, Some(English)); //! ``` //! //! All instances of [`LanguageDetector`](./struct.LanguageDetector.html) within a single //! application share the same language models and have synchronized access to them. //! So you can safely have multiple instances without worrying about consuming too much memory. //! //! ### 6.2 Minimum relative distance //! //! By default, *Lingua* returns the most likely language for a given input text. However, there are //! certain words that are spelled the same in more than one language. The word *prologue*, for //! instance, is both a valid English and French word. *Lingua* would output either English or //! French which might be wrong in the given context. For cases like that, it is possible to //! specify a minimum relative distance that the logarithmized and summed up probabilities for //! each possible language have to satisfy. It can be stated in the following way: //! //! ``` //! use lingua::LanguageDetectorBuilder; //! use lingua::Language::{English, French, German, Spanish}; //! //! let detector = LanguageDetectorBuilder::from_languages(&[English, French, German, Spanish]) //! .with_minimum_relative_distance(0.25) // minimum: 0.00 maximum: 0.99 default: 0.00 //! .build(); //! let detected_language = detector.detect_language_of("languages are awesome"); //! //! assert_eq!(detected_language, None); //! ``` //! //! Be aware that the distance between the language probabilities is dependent on the length of the //! input text. The longer the input text, the larger the distance between the languages. So if you //! want to classify very short text phrases, do not set the minimum relative distance too high. //! Otherwise [`None`](https://doc.rust-lang.org/std/option/enum.Option.html#variant.None) will be //! returned most of the time as in the example above. This is the return value for cases where //! language detection is not reliably possible. //! //! ### 6.3 Confidence values //! //! Knowing about the most likely language is nice but how reliable is the computed likelihood? //! And how less likely are the other examined languages in comparison to the most likely one? //! These questions can be answered as well: //! //! ``` //! use lingua::{LanguageDetectorBuilder, Language}; //! use lingua::Language::{English, French, German, Spanish}; //! use float_cmp::approx_eq; //! //! let languages = vec![English, French, German, Spanish]; //! let detector = LanguageDetectorBuilder::from_languages(&languages).build(); //! let confidence_values: Vec<(Language, f64)> = detector.compute_language_confidence_values( //! "languages are awesome" //! ); //! //! // The more readable version of the assertions below: //! // assert_eq!( //! // confidence_values, //! // vec![(English, 1.0), (French, 0.79), (German, 0.75), (Spanish, 0.72)] //! // ); //! //! assert_eq!(confidence_values[0], (English, 1.0_f64)); //! //! assert_eq!(confidence_values[1].0, French); //! assert!(approx_eq!(f64, confidence_values[1].1, 0.7917282993701181, ulps = 2)); //! //! assert_eq!(confidence_values[2].0, German); //! assert!(approx_eq!(f64, confidence_values[2].1, 0.7532048914992281, ulps = 2)); //! //! assert_eq!(confidence_values[3].0, Spanish); //! assert!(approx_eq!(f64, confidence_values[3].1, 0.7229637749926444, ulps = 2)); //! ``` //! //! In the example above, a vector of all possible languages is returned, sorted by their confidence //! value in descending order. The values that the detector computes are part of a **relative** //! confidence metric, not of an absolute one. Each value is a number between 0.0 and 1.0. //! The most likely language is always returned with value 1.0. All other languages get values //! assigned which are lower than 1.0, denoting how less likely those languages are in comparison //! to the most likely language. //! //! The vector returned by this method does not necessarily contain all languages which the calling //! instance of [`LanguageDetector`](./struct.LanguageDetector.html) was built from. //! If the rule-based engine decides that a specific language is truly impossible, then it will not //! be part of the returned vector. Likewise, if no ngram probabilities can be found within the //! detector's languages for the given input text, the returned vector will be empty. The confidence //! value for each language not being part of the returned vector is assumed to be 0.0. //! //! ### 6.4 Methods to build the LanguageDetector //! //! There might be classification tasks where you know beforehand that your language data is //! definitely not written in Latin, for instance (what a surprise :-). The detection accuracy can //! become better in such cases if you exclude certain languages from the decision process or just //! explicitly include relevant languages: //! //! ``` //! use lingua::{LanguageDetectorBuilder, Language, IsoCode639_1, IsoCode639_3}; //! //! // Including all languages available in the library //! // consumes approximately 2GB of memory and might //! // lead to slow runtime performance. //! LanguageDetectorBuilder::from_all_languages(); //! //! // Include only languages that are not yet extinct (= currently excludes Latin). //! LanguageDetectorBuilder::from_all_spoken_languages(); //! //! // Include only languages written with Cyrillic script. //! LanguageDetectorBuilder::from_all_languages_with_cyrillic_script(); //! //! // Exclude only the Spanish language from the decision algorithm. //! LanguageDetectorBuilder::from_all_languages_without(&[Language::Spanish]); //! //! // Only decide between English and German. //! LanguageDetectorBuilder::from_languages(&[Language::English, Language::German]); //! //! // Select languages by ISO 639-1 code. //! LanguageDetectorBuilder::from_iso_codes_639_1(&[IsoCode639_1::EN, IsoCode639_1::DE]); //! //! // Select languages by ISO 639-3 code. //! LanguageDetectorBuilder::from_iso_codes_639_3(&[IsoCode639_3::ENG, IsoCode639_3::DEU]); //! ``` #[macro_use] extern crate maplit; mod alphabet; mod builder; mod constant; mod detector; mod fraction; mod isocode; mod language; mod model; mod models; mod ngram; mod writer; pub use builder::LanguageDetectorBuilder; pub use detector::LanguageDetector; pub use isocode::{IsoCode639_1, IsoCode639_3}; pub use language::Language; pub use writer::{LanguageModelFilesWriter, TestDataFilesWriter}; #[cfg(test)] use regex::Regex; #[cfg(test)] pub(crate) fn minify(json: &str) -> String { let re = Regex::new("\n\\s*").unwrap(); re.replace_all(json, "").to_string() }