d2klab / ensemble-nerd

This repository aims to show ensemble methods over NERD extractors

ensemble-nerd

This repository aims to show two multilingual ensemble methods that combine the responses of web services NER and NED in order to improve the quality of the predicted entities. Both methods represent the information got by the extractor responses as real-valued vector (features engineering) and use Deep Neural Networks to produce the final output.

5 ensemble models have been built using the training set related to these gold standard:

• aida
• oke2015
• oke2016
• neel2015
• french subtitles corpus

Web API

The easiest way to try and use the ensemble nerd is via a Web API.

Version information

Version : 0.1.0

URI scheme

Host : http://enerd.eurecom.fr/

BasePath : TO_INSERT

Schemes : HTTP

Paths

POST /entities

Description

Extract, type and link entities from a document.

Request

The format in the HTTP header is respectively text/plain or application/json. In the second case, the input JSON has to be like this:

{
  "text":<PLAIN_TEXT_TO_BE_ANNOTATED>
}

Parameters

param	default	description
lang	auto	string containing ISO-639-2 language code
model_recognition	"oke2016"	model recognition model to be used
model_disambiguation	"oke2016"	model disambiguation model to be used

None of these parameters is mandatory. If the language is not specified, it is automatically detected by the program.

Example

A CURL POST request example is:

curl -X POST "TO_INSERT/entities?lang=en" -H "Content-type: application/json" -d '{"text":"In Italy the rector is the head of the university and Rappresentante Legale (Legal representative) of the university. He or she is elected by an electoral body."}'

It is identical to:

curl -X POST "http://127.0.0.1:5000/entities?lang=en" -H "Content-type: text/plain" -d "In Italy the rector is the head of the university and Rappresentante Legale (Legal representative) of the university. He or she is elected by an electoral body."

Response

The response format is application/json. An response example is showed here.

Train your own model

Follow the guide inside the app folder.

Docker setup

Build

docker build -t d2klab/ensemble-nerd .

Run

docker run -d -p 8089:8089  -v /Users/pasquale/git/ensemble-nerd/app/data:/data --name enerd d2klab/ensemble-nerd

Stop

docker stop enerd
docker rm enerd ##remove from available containers
docker rmi d2klab/ensemble-nerd ##remove from images