Dict2vec

Dict2vec is a framework to learn word embeddings using lexical dictionaries.

Requirements

To compile and run our Dict2vec model, you will need :

gcc (4.8.4 or newer)
make

To evaluate the learned embeddings on the word similarity task, you will need :

python3
numpy (python3 version)
scipy (python3 version)

To fetch definitions from online dictionaries, you will need :

python3

To run demo scripts and download training data, you will also need a machine with wget, bzip2, perl and bash installed.

Run the code

Before running the example script, open demo-train.sh and modify the line 62 so the variable THREADS is equal to the number of cores in your machine. By default, it is equal to 8, so if your machine only has 4 cores, update it to be :

THREADS=4

Then run demo-train.sh to have a quick glimpse of Dict2vec performances.

$ ./demo-train.sh

This will :

download a training file of 50M words
download strong and weak pairs for training
compile Dict2vec source code into a binary executable
train word embeddings with a dimension of 100
evaluate the embeddings on 11 word similarity datasets

To directly compile the code and interact with the sotfware, run :

$ make
$ ./dict2vec

Full documentation of each possible parameters is displayed when you run ./dict2vec without any arguments.

Evaluate word embeddings

Run evaluate.py to evaluate a trained word embedding. Once the evaluation is done, you get something like this :

$ ./evaluate.py embeddings.txt
Filename        | AVG  | MIN  | MAX  | STD  | oov
===================================================
MC-30.txt       | 0.848| 0.848| 0.848| 0.000|  0%
MEN-TR-3k.txt   | 0.726| 0.726| 0.726| 0.000|  0%
MTurk-287.txt   | 0.659| 0.659| 0.659| 0.000|  0%
MTurk-771.txt   | 0.684| 0.684| 0.684| 0.000|  0%
RG-65.txt       | 0.833| 0.833| 0.833| 0.000|  0%
RW-STANFORD.txt | 0.503| 0.503| 0.503| 0.000|  36%
SimVerb-3500.txt| 0.399| 0.399| 0.399| 0.000|  3%
WS-353-ALL.txt  | 0.743| 0.743| 0.743| 0.000|  0%
WS-353-REL.txt  | 0.670| 0.670| 0.670| 0.000|  0%
WS-353-SIM.txt  | 0.763| 0.763| 0.763| 0.000|  0%
YP-130.txt      | 0.630| 0.630| 0.630| 0.000|  3%
---------------------------------------------------
W.Average       | 0.577

The script computes the Spearman's rank correlation score for some word similarity datasets, as well as the OOV rate for each dataset and the weighted average based on the number of pairs evaluated on each dataset. We provide the evaluation datasets in data/eval/.

MC-30 (Miller and Charles, 1991)
MEN (Bruni et al., 2014)
MTurk-287 (Radinsky et al., 2011)
MTurk-771 (Halawi et al., 2012)
RG-65 (Rubenstein and Goodenough, 1965)
RW (Luong et al., 2013)
SimVerb-3500 (Gerz et al., 2016)
WordSim-353 (Finkelstein et al., 2001)
YP-130 (Yang and Powers, 2006)

This script is also able to evaluate several embeddings files at the same time, and compute the average score as well as the standard deviation. To evaluate several embeddings, simply add the filenames as arguments :

$ ./evaluate.py embedding-1.txt embedding-2.txt embedding-3.txt

This script will report :

AVG : the average score of all embeddings for each dataset
MIN : the minimum score of all embeddings for each dataset
MAX : the maximum score of all embeddings for each dataset
STD : the standard deviation score of all embeddings for each dataset

When you evaluate only one embedding, you get the same value for AVG/MIN/MAX and a standard deviation STD of 0.

Download pre-trained vectors

We provide word embeddings trained with the Dict2vec model on the July 2017 English version of Wikipedia. Vectors with dimension 100 (resp. 200) were trained on the first 50M (resp. 200M) words of this corpus whereas vectors with dimension 300 were trained on the full corpus. First line is composed of (number of words / dimension). Each following line contain the word and all its space separated vector values.

You need to extract the embeddings before using them. Use the following command to do so :

$ tar xvjf dict2vec100.tar.bz2

If you use these word embeddings, please cite the paper as explained in section Cite this paper.

dimension 100 (85MB)
dimension 200 (354MB)
dimension 300 (4.3GB)

Download more data

Definitions

We provide scripts to download online definitions and generate strong/weak pairs based on these definitions. More information and full documentation can be found here.

Wikipedia

You can generate the same 3 files (50M, 200M and full) we use for training in the paper by running the script wiki-dl.sh.

$ ./wiki-dl.sh

This script will download the full English Wikipedia dump of July 2017, uncompress it and directly feed it into Mahoney's parser script. It also cuts the entire dump into two smaller datasets : one containing the first 50M tokens (enwiki-50M), and the other one containing the first 200M tokens (enwiki-200M). We report the following filesizes :

enwiki-50M : 283MB
enwiki-200M : 1.1GB
enwiki-full : 23GB

Cite this paper

Please cite this paper if you use our code to learn word embeddings or download definitions or use our pre-trained word embeddings.

J. Tissier, C. Gravier, A. Habrard, Dict2vec : Learning Word Embeddings using Lexical Dictionaries

@inproceedings{tissier2017dict2vec,
  title     = {Dict2vec : Learning Word Embeddings using Lexical Dictionaries},
  author    = {Tissier, Julien and Gravier, Christophe and Habrard, Amaury},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  pages     = {254--263},
  year      = {2017}
}

License

This project is licensed under the GNU GPL v3 license. See the LICENSE file for details.

tombosc/dict2vec