/ned-graphs

Primary LanguagePython

Code and Dataset for Named Entity Disambiguation using Deep Learning on Graphs

This repository contains the code and dataset for the paper "Named Entity Disambiguation using Deep Learning on Graphs". The full paper can be found here.

Installation

The main requirements are installed with:

virtualenv --python=/usr/bin/python3 .env
source .env/bin/activate
pip install -r requirements.txt

Download the glove vector files

cd data
wget http://nlp.stanford.edu/data/glove.840B.300d.zip
unzip glove.840B.300d.zip
echo "2196017 300" | cat - glove.840B.300d.txt > glove_2.2M.txt
cd ..

One must also unzip the mapping of the wikidata nodes

cd data
bunzip2 x*
cat x* > wikidata_items.csv
cd ..

Running the models

In order to train the system

cd <MODEL_NAME>
python -m wikidata_query.train

Similarly for testing

cd <MODEL_NAME>
python -m wikidata_query.test

Citing the paper


@ARTICLE{2018arXiv181009164C,
   author = {{Cetoli}, A. and {Akbari}, M. and {Bragaglia}, S. and {O'Harney}, A.~D. and 
	{Sloan}, M.},
    title = "{Named Entity Disambiguation using Deep Learning on Graphs}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1810.09164},
 primaryClass = "cs.CL",
 keywords = {Computer Science - Computation and Language},
     year = 2018,
    month = oct,
   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv181009164C},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}