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[Utility & Helpers](/categories/utility) 4. / 5. jbainpro/mitie ActiveLibrary[Utility & Helpers](/categories/utility) jbainpro/mitie ============== Named-entity recognition for PHP 01PHP Since Nov 14Pushed 2y agoCompare [ Source](https://github.com/jbainpro/mitie-nlp-php)[ Packagist](https://packagist.org/packages/jbainpro/mitie)[ RSS](/packages/jbainpro-mitie/feed)WikiDiscussions master Synced 1mo ago READMEChangelogDependenciesVersions (1)Used By (0) MITIE PHP ========= [](#mitie-php) [MITIE](https://github.com/mit-nlp/MITIE) - named-entity recognition, binary relation detection, and text categorization - for PHP - Finds people, organizations, and locations in text - Detects relationships between entities, like `PERSON` was born in `LOCATION` [![Build Status](https://github.com/ankane/mitie-php/workflows/build/badge.svg?branch=master)](https://github.com/ankane/mitie-php/actions) Installation ------------ [](#installation) Run: ``` composer require ankane/mitie ``` Download the shared library: ``` composer exec -- php -r "require 'vendor/autoload.php'; Mitie\Vendor::check(true);" ``` And download the pre-trained models for your language: - [English](https://github.com/mit-nlp/MITIE/releases/download/v0.4/MITIE-models-v0.2.tar.bz2) - [Spanish](https://github.com/mit-nlp/MITIE/releases/download/v0.4/MITIE-models-v0.2-Spanish.zip) - [German](https://github.com/mit-nlp/MITIE/releases/download/v0.4/MITIE-models-v0.2-German.tar.bz2) Getting Started --------------- [](#getting-started) - [Named Entity Recognition](#named-entity-recognition) - [Binary Relation Detection](#binary-relation-detection) - [Text Categorization](#text-categorization) Named Entity Recognition ------------------------ [](#named-entity-recognition) Load an NER model ``` $model = new Mitie\NER('ner_model.dat'); ``` Create a document ``` $doc = $model->doc('Nat works at GitHub in San Francisco'); ``` Get entities ``` $doc->entities(); ``` This returns ``` [ ['text' => 'Nat', 'tag' => 'PERSON', 'score' => 0.3112371212688382, 'offset' => 0], ['text' => 'GitHub', 'tag' => 'ORGANIZATION', 'score' => 0.5660115198329334, 'offset' => 13], ['text' => 'San Francisco', 'tag' => 'LOCATION', 'score' => 1.3890524313885309, 'offset' => 23] ] ``` Get tokens ``` $doc->tokens(); ``` Get tokens and their offset ``` $doc->tokensWithOffset(); ``` Get all tags for a model ``` $model->tags(); ``` ### Training [](#training) Load an NER model into a trainer ``` $trainer = new Mitie\NERTrainer('total_word_feature_extractor.dat'); ``` Create training instances ``` $tokens = ['You', 'can', 'do', 'machine', 'learning', 'in', 'PHP', '!']; $instance = new Mitie\NERTrainingInstance($tokens); $instance->addEntity(3, 4, 'topic'); // machine learning $instance->addEntity(6, 6, 'language'); // PHP ``` Add the training instances to the trainer ``` $trainer->add($instance); ``` Train the model ``` $model = $trainer->train(); ``` Save the model ``` $model->saveToDisk('ner_model.dat'); ``` Binary Relation Detection ------------------------- [](#binary-relation-detection) Detect relationships betweens two entities, like: - `PERSON` was born in `LOCATION` - `ORGANIZATION` was founded in `LOCATION` - `FILM` was directed by `PERSON` There are 21 detectors for English. You can find them in the `binary_relations` directory in the model download. Load a detector ``` $detector = new Mitie\BinaryRelationDetector('rel_classifier_organization.organization.place_founded.svm'); ``` And create a document ``` $doc = $model->doc('Shopify was founded in Ottawa'); ``` Get relations ``` $detector->relations($doc); ``` This returns ``` [['first' => 'Shopify', 'second' => 'Ottawa', 'score' => 0.17649169745814464]] ``` ### Training [](#training-1) Load an NER model into a trainer ``` $trainer = new Mitie\BinaryRelationTrainer($model); ``` Add positive and negative examples to the trainer ``` $tokens = ['Shopify', 'was', 'founded', 'in', 'Ottawa']; $trainer->addPositiveBinaryRelation($tokens, [0, 0], [4, 4]); $trainer->addNegativeBinaryRelation($tokens, [4, 4], [0, 0]); ``` Train the detector ``` $detector = $trainer->train(); ``` Save the detector ``` $detector->saveToDisk('binary_relation_detector.svm'); ``` Text Categorization ------------------- [](#text-categorization) Load a model into a trainer ``` $trainer = new Mitie\TextCategorizerTrainer('total_word_feature_extractor.dat'); ``` Add labeled text to the trainer ``` $trainer->add('This is super cool', 'positive'); ``` Train the model ``` $model = $trainer->train(); ``` Save the model ``` $model->saveToDisk('text_categorization_model.dat'); ``` Load a saved model ``` $model = new Mitie\TextCategorizer('text_categorization_model.dat'); ``` Categorize text ``` $model->categorize('What a super nice day'); ``` History ------- [](#history) View the [changelog](CHANGELOG.md) Contributing ------------ [](#contributing) Everyone is encouraged to help improve this project. Here are a few ways you can help: - [Report bugs](https://github.com/ankane/mitie-php/issues) - Fix bugs and [submit pull requests](https://github.com/ankane/mitie-php/pulls) - Write, clarify, or fix documentation - Suggest or add new features To get started with development: ``` git clone https://github.com/ankane/mitie-php.git cd mitie-php composer install composer test ``` ### Health Score 13 — LowBetter than 1% of packages Maintenance20 Infrequent updates — may be unmaintained Popularity1 Limited adoption so far Community8 Small or concentrated contributor base Maturity20 Early-stage or recently created project Bus Factor1 Top contributor holds 87% of commits — single point of failure How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window. **Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores. **Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement. **Maturity (30%)** — Project age, version count, PHP version support, and release stability. ### Community Maintainers ![](https://www.gravatar.com/avatar/eb1bdeb4ee185cc8c56b9f60a1fdcc65cfbdef6f3e9107242e4e1807bed7b712?d=identicon)[jbainpro](/maintainers/jbainpro) --- Top Contributors [![ankane](https://avatars.githubusercontent.com/u/220358?v=4)](https://github.com/ankane "ankane (20 commits)")[![jbainpro](https://avatars.githubusercontent.com/u/60293260?v=4)](https://github.com/jbainpro "jbainpro (3 commits)") ### Embed Badge ![Health badge](/badges/jbainpro-mitie/health.svg) ``` [![Health](https://phpackages.com/badges/jbainpro-mitie/health.svg)](https://phpackages.com/packages/jbainpro-mitie) ``` ### Alternatives [webdevstudios/oops-wp A collection of interfaces and class structures to promote object-oriented programming practices in WordPress. 5820.7k1](/packages/webdevstudios-oops-wp) PHPackages © 2026 [Directory](/)[Categories](/categories)[Trending](/trending)[Changelog](/changelog)[Analyze](/analyze)