Alenka is a free library for predicting Russian names (and genders) from transliterated and short forms with statistical learning and Levenshtein distance.

Alenka now is not a tool for inflection (see Lvovich) or parsing first/middle/last Russian names from strings. But it will somewhen be.

Useful for social data and media analysis.

🌀 Natively in TypeScript

🔢 Statistically trained

❤ Simple and friendly API

⚙ Configurable

Install Alenka with npm or yarn.

npm i alenka
yarn add alenka

Using with default configuration

import { NamePredictor } from "alenka";

const np = new NamePredictor();

np.predictName('Саня') // 'Александр' as more frequent
np.predictName('Саня', 'female') // 'Александра'
np.predictName('Сирожа') // 'Сергей' due to Levenstein matching
np.predictName('$one4ka') // 'София' due to transliteration rules
np.predictName('asdasda') // undefined
np.predictGender('Соня') // 'female'
np.predictAll('Саня') // [{name: 'Александр', ...}, ...]

Using with custom configuration

import { NamePredictor } from "alenka";

const options = {
    useLevenshtein: false,
    // minAcceptibleDistance: 1,
    // frequenciesSource: 'vk',
    // handlingCase: 'ignore',
    // handlingYo: 'ignore',
} as const;

const np = new NamePredictor(options);

np.predictName('Сирожа') // 'unedfined'

1. Names are full forms of Russian Names (e.g. Александр or София);
2. Shorts are shotened forms of names (e.g. Саша or Сонечка);
3. Transliterations are latin versions of names or shorts (e.g. Alex or Sofia);
4. Variants are names, shorts and transliterations together;
5. Tokens are any strings including variatns (e.g. Саша, $0nechk@, abcdefg).

Prediction is mapping a token to a name. It happens via mathcing token against a dataset of names, shorts and transliterations with Levenshtein distance to find the closest one and return the relevant name. If nothing matched, undefined will be returned.

Feel free to fork and leave pull requests.

To start development just clone the repo and run following familiar commands.

yarn install
yarn test:watch

Here a list of actual tasks to participate at. Thanks in advance for any help.

• Fix problems with handling 'ё'
• Split in the dataset variants for shorts and tranliterations
• Finish README
• Add issues board (bug tracker)
• Add ESLint
• Add missing short forms to the data source
• Write transliteration logic (instead of cyrillic-to-translit-js)
• Optimize performance

I am Sergei Solovev, fullstack TS developer, data analyst and teacher. Look at my works here. For fast response get me on telegram

Copyright (c) 2022, Sergei Solovev sergey.soloviev@inbox.ru

Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.