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
The easiest way to try and use the ensemble nerd is via a Web API.
Version : 0.1.0
Host : http://enerd.eurecom.fr/
BasePath : TO_INSERT
Schemes : HTTP
Extract, type and link entities from a document.
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>
}
| 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.
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."
The response format is application/json. An response example is showed here.
Follow the guide inside the app folder.
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