Distributed skipgram mixture model for multisense word embedding
Перейти к файлу
ustctf 4a79d12f28 Modify the running scripts. 2015-11-20 11:18:32 +08:00
scripts Modify the running scripts. 2015-11-20 11:18:32 +08:00
src Change IO. 2015-11-08 15:14:58 +08:00
windows/distributed_skipgram_mixture disable crt warning 2015-11-06 17:16:07 +08:00
.gitignore init commit 2015-10-14 12:16:19 +08:00
LICENSE add LICENSE 2015-10-16 15:13:40 +08:00
Makefile Update Makefile 2015-11-11 18:59:59 +08:00
README.md Modify the running scripts. 2015-11-20 11:18:32 +08:00
build.sh linux build 2015-11-06 17:19:43 +08:00

README.md

Distributed Multisense Word Embedding

The Distributed Multisense Word Embedding(DMWE) tool is a parallelization of the Skip-Gram Mixture [1] algorithm on top of the DMTK parameter server. It provides an efficient "scaling to industry size" solution for multi sense word embedding.

For more details, please view our website http://www.dmtk.io

Download

$ git clone https://github.com/Microsoft/distributed_skipgram_mixture

Build

Prerequisite

DMWE is built on top of the DMTK parameter sever, therefore please download and build DMTK first (https://github.com/Microsoft/multiverso).

For Windows

Open windows\distributed_skipgram_mixture\distributed_skipgram_mixture.sln using Visual Studio 2013. Add the necessary include path (for example, the path for DMTK multiverso) and lib path. Then build the solution.

For Ubuntu (Tested on Ubuntu 12.04)

Download and build by running $ sh build.sh. Modify the include and lib path in Makefile. Then run $ make all -j4.

Run

For parameter settings, see scripts/parameters_settings.txt. For running it, see the example script scripts/run.py.

Reference

[1] Tian, F., Dai, H., Bian, J., Gao, B., Zhang, R., Chen, E., & Liu, T. Y. (2014). A probabilistic model for learning multi-prototype word embeddings. In Proceedings of COLING (pp. 151-160).