- for Ubuntu platform please first install python3-tk
sudo apt-get install python3-tk
- for Ubuntu or Mac platform type:
$ git clone https://github.com/gnodisnait/nball4tree.git
$ cd nball4tree
$ virtualenv venv
$ source venv/bin/activate
$ pip install -r requirements.txt
- datasets for training nball embeddings
- download glove.6B.50d.txt from the GloVe webpage https://nlp.stanford.edu/projects/glove/
- shell command for running the nball construction and training process
% you need to create an empty file nball.txt for output
$ python nball.py --train_nball /Users/<user-name>/data/glove/nball.txt --w2v /Users/<user-name>/data/glove/glove.6B.50d.txt --ws_child /Users/<user-name>/data/glove/wordSenseChildren47634.txt --ws_catcode /Users/<user-name>/data/glove/glove.6B.catcode.txt --log log.txt
% --train_nball: output file of nball embeddings
% --w2v: file of pre-trained word embeddings
% --ws_child: file of parent-children relations among word-senses
% --ws_catcode: file of the parent location code of a word-sense in the tree structure
% --log: log file, shall be located in the same directory as the file of nball embeddings
The training process can take around 6.5 hours.
- main input is the output directory of nballs created in Experiment 1.1
- shell command for running the nball construction and training process
$ python nball.py --zero_energy <output-path> --ball <output-file> --ws_child /Users/<user-name>/data/glove/wordSenseChildren.txt
% --zero_energy <output-path> : output path of the nballs of Experiment 1.1, e.g. ```/Users/<user-name>/data/glove/data_out```
% --ball <output-file> : the name of the output nball-embedding file
% --ws_child /Users/<user-name>/data/glove/wordSenseChildren.txt: file of parent-children relations among word-senses
The checking process can take around 2 hours.
- result
If zero-energy is achieved, a big nball-embedding file will be created <output-path>/<output-file>
otherwise, failed relations and word-senses will be printed.
** Test result at Mac platform:
** Test result at Ubuntu platform:
$ python nball.py --neighbors beijing.n.01 berlin.n.01 --ball /Users/<user-name>/data/glove/glove.6B.50Xball.V10.txt --num 6
% --neighbors: list of word-senses
% --ball: file location of the nball embeddings
% --num: number of neighbors
- Results of nearest neighbors look like below:
-
shell command for running the experiment
$ python nball.py --std_stem /Users/<user-name>/data/glove/wordstem.std --dim 50 --w2v /Users/<user-name>/data/glove/glove.6B.50d.txt --ballStemFile /Users/<user-name>/data/glove/glove.6B.50Xball.words --ball /Users/<user-name>/data/glove/glove.6B.50Xball.V10.txt
- Result of consistency analysis
- datasets for validating unknown word-sense or words
- shell command for running the experiment
$ python nball.py --validate_member /Users/<user-name>/data/glove/memberValidation/membershipPredictionResult.txt \
--numOfChild 10 --percentages 5 10 20 30 40 50 60 70 80 90 \
--taskFiles /Users/<user-name>/data/glove/memberValidation/membershipPredictionTask.txt \
--w2v /Users/<user-name>/data/glove/glove.6B.50d.txt \
--ws_child /Users/<user-name>/data/glove/wordSenseChildren.txt \
--ws_path /Users/<user-name>/data/glove/wordSensePath.txt \
--ws_catcode /Users/<user-name>/data/glove/glove.6B.catcode.txt \
--logPath /Users/<user-name>/data/glove/logMemberValidate
- command for viewing the result of validating unknown word-sense or word
$ python nball.py --plot_validate_member /Users/<user-name>/data/glove/memberValidation/membershipPredictionResult.txt --numOfChild 10 --percentages 5 10 20 30 40 50 60 70 80 90
- Precision of validating the category of unknown words
- Recall of validating the category of unknown words
If you use the code, please cite the following paper:
Tiansi Dong, Chrisitan Bauckhage, Hailong Jin, Juanzi Li, Olaf Cremers, Daniel Speicher, Armin B. Cremers, Joerg Zimmermann (2019). Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction. ICLR-19 The Seventh International Conference on Learning Representations, May 6 – 9, New Orleans, Louisiana, USA.
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