#Interpretable Word Embeddings
##Overview This tool provide a method of interpretable word embeddings, which is based on OIWE-IPG model in our paper, Online Learning of Interpretable Word Embeddings.
##Usage
####In Directory word2nve-c/: Use command "make" to compile sourse files in directory. OIWE model is realized in word2nvec.c
####In Directory bin/: The command to run OIWE model is in launcher.sh. Configurations in launcher.sh can be modified. Use "sh launcher.sh" to run the model.
format: ./word2vec -train -output -size -threads -binary <save as binary: 0 or 1> -cbow 0
##Experiments
####Data
- text8 corpus from word2vec website: https://code.google.com/p/word2vec/
- Test datasets on http://wordvectors.org
####Word Similarty Task
| Task name | Word pairs | Pairs found | OIWE-IPG | OIWE-NPG | Skip-gram | RNN |
|---|---|---|---|---|---|---|
| WS-353 | 353 | 351 | 0.6415 | 0.5833 | 0.6380 | 0.3675 |
| WS-353-SIM | 203 | 202 | 0.7174 | 0.6371 | 0.6735 | 0.4928 |
| WS-353-REL | 252 | 251 | 0.6032 | 0.5788 | 0.6039 | 0.2925 |
| MC-30 | 30 | 30 | 0.6245 | 0.5245 | 0.5041 | 0.5826 |
| RG-65 | 65 | 65 | 0.5716 | 0.5685 | 0.5049 | 0.5019 |
| Rare-Word | 2034 | 951 | 0.3379 | 0.2544 | 0.3694 | 0.3904 |
| MEN | 3000 | 2987 | 0.5760 | 0.5668 | 0.5256 | 0.4344 |
| MTurk-287 | 287 | 284 | 0.6054 | 0.5350 | 0.5848 | 0.5093 |
| MTurk-771 | 771 | 769 | 0.4981 | 0.4147 | 0.4965 | 0.3990 |
| YP-130 | 139 | 118 | 0.3066 | 0.1808 | 0.2493 | 0.4021 |
####Spearman Coefficient - dimension
| Dimension Number | OIWE | Skip-gram |
|---|---|---|
| 100 | 62.851 | 64.724 |
| 200 | 66.45 | 64.387 |
| 300 | 71.74 | 67.35 |
| 400 | 66.74 | 64.82 |
| 500 | 64.118 | 66.046 |
####Word Intrusion
| Model | Precision(%) |
|---|---|
| Skip-gram | 32.62 |
| NNSE | 92.00 |
| OIWE-NPG | 61.40 |
| OIWE-IPG | 94.80 |