Interpretable-contrastive-word-mover-s-embedding

Paper Datasets

Here is a Dropbox link to the datasets used in the paper: https://www.dropbox.com/sh/nf532hddgdt68ix/AABGLUiPRyXv6UL2YAcHmAFqa?dl=0 The dataset in the above link was provided in .mat file. You may need to transform to the .npy file to run our code. Each mat file contains following component
X is a cell array of all documents, each represented by a dxm matrix where d is the dimensionality of the word embedding and m is the number of unique words in the document. ("BBCsports.npy")
Y is an array of labels ("BBCsports_grade.npy")
BOW_X is a cell array of word counts for each document('weight.npy')
indices is a cell array of global unique IDs for words in a document
TR is a matrix whose ith row is the ith training split of document indices('index_tr.npy')
TE is a matrix whose ith row is the ith testing split of document indices('index_te.npy')
'BBCsports_length.npy' is the number of unique words for each sample.

Demo

In the demo code we use BBCsports data set. The data is preprocessed and has been saved as .npy file can be found in the following link: https://drive.google.com/drive/folders/1GuQsHS1J8J24GnCmTCTDPH5hWWYtmw4s?usp=sharing
Please put the data into the same path as 2 python files.
Use

python run_pos.py

to run the file.

Citation

If you find this repo useful for your research, please consider citing the paper

@misc{jiang2021interpretable,
    title={Interpretable contrastive word mover's embedding},
    author={Ruijie Jiang and Julia Gouvea and Eric Miller and David Hammer and Shuchin Aeron},
    year={2021},
    eprint={2111.01023},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Any question please feel free to contact Ruijie Jiang ([email protected]).