english 0.0.10

English inflector decliner conjugator from wiktionary data
Documentation

english

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english is a blazing fast English morphology library written in Rust with zero external dependencies and a total code+data size under 1 MB. It provides extremely accurate verb conjugation and noun/adjective declension based on highly processed Wiktionary data, making it ideal for real-time procedural text generation.

⚡ Speed and Accuracy

Evaluation of the English inflector (extractor/main.rs/check_*) and performance benchmarking (examples/speedmark.rs) shows:

Part of Speech Correct / Total Accuracy Throughput (calls/sec) Time per Call
Nouns 238106 / 238549 99.81% 3,929,101 254 ns
Verbs 158056 / 161643 97.78% 5,572,956 180 ns
Adjectives 119200 / 119356 99.86% 7,167,281 139 ns

Note: Benchmarking was done under a worst-case scenario; typical real-world usage is 50-100 ns faster.

📦 Installation

Add to your Cargo.toml:

[dependencies]
english = "0.0.9"

Then in your code:

use english::*;
fn main() {
    // --- Mixed Sentence Example ---
    let subject_number = Number::Plural;
    let run = Verb::present_participle("run"); // running
    let child = Noun::from("child").with_specifier(run); //running child
    let subject = English::noun(child, &subject_number); //running children
    let verb = English::verb(
        "steal",
        &Person::Third,
        &subject_number,
        &Tense::Past,
        &Form::Finite,
    ); //stole
    let object = Noun::count_with_number("potato", 7); //7 potatoes

    let sentence = format!("The {} {} {}.", subject, verb, object);
    assert_eq!(sentence, "The running children stole 7 potatoes.");

    // --- Nouns ---
    // Note that noun(), count(), etc can work on both strings and Noun struct
    let jeans = Noun::from("pair").with_complement("of jeans");
    assert_eq!(Noun::count_with_number(jeans, 3), "3 pairs of jeans");
    // Regular plurals
    assert_eq!(English::noun("cat", &Number::Plural), "cats");
    // Add a number 2-9 to the end of the word to try different forms.
    // Can use plural()
    assert_eq!(Noun::plural("die2"), "dice");
    // Use count function for better ergonomics if needed
    assert_eq!(Noun::count("man", 2), "men");
    // Use count_with_number function to preserve the number
    assert_eq!(Noun::count_with_number("nickel", 3), "3 nickels");
    // Invariant nouns
    assert_eq!(English::noun("sheep", &Number::Plural), "sheep");

    // --- Verbs ---
    // All verb functions can use either strings or Verb struct
    let pick_up = Verb::from("pick").with_particle("up");
    // Helper functions: past() , third_person(), present_participle(), infinitive() etc.
    assert_eq!(Verb::past(&pick_up,), "picked up");
    assert_eq!(Verb::present_participle("walk"), "walking");
    assert_eq!(Verb::past_participle("go"), "gone");
    // Add a number 2-9 to the end of the word to try different forms.
    assert_eq!(Verb::past("lie"), "lay");
    assert_eq!(Verb::past("lie2"), "lied");
    // "to be" has the most verb forms in english and requires using verb()
    assert_eq!(
        English::verb(
            "be",
            &Person::First,
            &Number::Singular,
            &Tense::Present,
            &Form::Finite
        ),
        "am"
    );

    // --- Adjectives ---
    // Add a number 2-9 to the end of the word to try different forms. (Bad has the most forms at 3)
    assert_eq!(English::adj("bad", &Degree::Comparative), "more bad");
    assert_eq!(English::adj("bad", &Degree::Superlative), "most bad");
    assert_eq!(Adj::comparative("bad2"), "badder");
    assert_eq!(Adj::superlative("bad2"), "baddest");
    assert_eq!(Adj::comparative("bad3"), "worse");
    assert_eq!(Adj::superlative("bad3"), "worst");
    assert_eq!(Adj::positive("bad3"), "bad");

    // --- Pronouns ---
    assert_eq!(
        English::pronoun(
            &Person::First,
            &Number::Singular,
            &Gender::Neuter,
            &Case::PersonalPossesive
        ),
        "my"
    );
    assert_eq!(
        English::pronoun(
            &Person::First,
            &Number::Singular,
            &Gender::Neuter,
            &Case::Possessive
        ),
        "mine"
    );

    // --- Possessives ---
    assert_eq!(English::add_possessive("dog"), "dog's");
    assert_eq!(English::add_possessive("dogs"), "dogs'");
}

🔧 Crate Overview

english

The public API for verb conjugation and noun/adjective declension.

  • Combines optimized data generated from extractor with inflection logic from english-core
  • Pure Rust, no external dependencies
  • Fast Binary search over pre-sorted arrays: O(log n) lookup.
  • Code generation ensures no runtime penalty.

english-core

The core engine for English inflection — pure algorithmic logic.

  • Implements the core rules for conjugation/declension
  • Used to classify forms as regular or irregular for the extractor
  • Has no data dependency — logic-only
  • Can be used stand alone for an even smaller footprint (at the cost of some accuracy)

extractor

A tool to process and refine Wiktionary data.

  • Parses large English Wiktionary dumps
  • Extracts all verb, noun, and adjective forms
  • Uses english-core to filter out regular forms, preserving only irregulars
  • Generates sorted static arrays for use in english

📦 Obtaining Wiktionary Data & Running the Extractor

This project relies on raw data extracted from Wiktionary. Current version built with data from 8/17/2025.

Steps

  1. Download the raw Wiktextract JSONL dump (~20 GB) from Kaikki.org.
  2. Place the file somewhere accessible (e.g. ../rawwiki.jsonl).
  3. From the extractor folder, run: cargo run --release ../rawwiki.jsonl
  4. Move the generated files adj_array.rs, noun_array.rs, verb_array.rs into the /src of english

Benchmarks

Performance benchmarks were run on my M2 Macbook.

Writing benchmarks and tests for such a project is rather difficult and requires opinionated decisions. Many words may have alternative inflections, and the data in wiktionary is not perfect. Many words might be both countable and uncountable, the tagging of words may be inconsistent. This library includes a few uncountable words in its dataset, but not all. Uncountable words require special handling anyway. Take all benchmarks with a pound of salt, write your own tests for your own usecases. Any suggestions to improve the benchmarking are highly appreciated.

Disclaimer

Wiktionary data is often unstable and subject to weird changes. This means that the provided inflections may change unexpectedly. You can look at the diffs of *_array.rs files for a source of truth.

Inspirations

📄 License