Warning: I am no longer using this library and it is not supported. I'd suggest forking it or looking for an actively maintained library.
This project is a node binding for the MIT Information Extraction library. It's written in C++ and js.
I've implemented both the Named Entity Recognition extractor and the Binary Relation detector.
The purpose of this library is to extract information from free-form text. It allows us to extract two different types of information.
Named Entities are typically a (person, location, organization, etc) but you can train your own model to find any type of named entity.
For example, suppose we take the sentence:
Barack Obama was born in Hawaii and graduated from Harvard Law School in Cambridge Massachusetts.
This library can extract and tag the named entities:
person: Barack Obama
location: Hawaii
organization: Harvard Law School
location: Cambridge Massachusetts
Binary Relations allow us to predict relations bewteen our named entity.
We must use different models for each relationship (MITIE comes with a number of classifiers but you can also train your own).
As an example, suppose we run our example sentence through the model binary_relations/rel_classifier_people.person.place_of_birth.svm
.
It would show us that Entity 'Barack Obama' is related to Entity 'Hawaii'.
Barack Obama <born in> Hawaii
var mitie = require('mitie');
// Point it at a ner model .dat file
// You can also find where to get these on the main MIT project repo
var nerFile = '/usr/local/Cellar/mitie/0.4/share/MITIE-models/english/ner_model.dat';
var neExtractor = new mitie.EntityExtractor(nerFile);
// Feed in some data to the predict method
var data = "Barack Obama visited New Orleans today for the anniversary of Hurricane Katrina."
var results = neExtractor.predict(data);
/**
* results => {
* tags:
* [ { tagNumber: 0,
* score: 1.0957487452879051,
* tagType: 'PERSON',
* tokens: 'Barack Obama' },
* { tagNumber: 1,
* score: 0.8764343236070008,
* tagType: 'LOCATION',
* tokens: 'New Orleans' } ] }
*/
var relFile = '/usr/local/Cellar/mitie/0.4/share/MITIE-models/english/binary_relations/rel_classifier_people.person.place_of_birth.svm';
var relExtractor = new mitie.RelationExtractor(nerFile, relFile);
data = "Donald Trump says that Barack Obama was born in Kenya, not Hawaii.";
results = relExtractor.predict(data);
/**
* results => {
* tags:
* [ { tagNumber: 0, tagType: 'PERSON', tokens: 'Donald Trump' },
* { tagNumber: 0, tagType: 'PERSON', tokens: 'Barack Obama' },
* { tagNumber: 1, tagType: 'LOCATION', tokens: 'Kenya' },
* { tagNumber: 1, tagType: 'LOCATION', tokens: 'Hawaii' } ],
* relations:
* [ { score: 0.5990590608043147,
* first: 'Barack Obama',
* second: 'Kenya' } ] }
*/
The quickest way to get the mitie dependency is with homebrew:
brew install mitie
You can install with npm on the command line or in your package.json
:
npm install mitie --save
Install with development deps:
npm install --dev
Set two env vars however you like (you can also inline):
export NER_MODEL=/usr/local/Cellar/mitie/0.4/share/MITIE-models/english/ner_model.dat
export REL_MODEL=/usr/local/Cellar/mitie/0.4/share/MITIE-models/english/binary_relations/rel_classifier_people.person.place_of_birth.svm
Run mocha tests:
npm test
- No error handling on C++ level
- The the js API is not too good and it's not very efficient
- Consider implementing trainers