Paper title: Dynamic Word Embeddings for Evolving Semantic Discovery.
Paper links: https://dl.acm.org/citation.cfm?id=3159703 https://arxiv.org/abs/1703.00607
Files:
/embeddings
- embeddings in loadable MATLAB files. 0 corresponds to 1990, 1 to 1991, ..., 19 to 2009. To save space, each year's embedding is saved separately. When used in visualization code, first merge to 1 embedding file.
/train_model
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contains code used for training our embeddings
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data file download: https://www.dropbox.com/s/nifi5nj1oj0fu2i/data.zip?dl=0
/train_model/train_time_CD_smallnyt.py
- main training script
/train_model/util_timeCD.py
- containing helper functions
/other_embeddings
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contains code for training baseline embeddings
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data file download: https://www.dropbox.com/s/tzkaoagzxuxtwqs/data.zip?dl=0
/other_embeddings/staticw2v.py
- static word2vec (Mikolov et al 2013)
/other_embeddings/aw2v.py
- aligned word2vec (Hamilton, Leskovec, Jufarsky 2016)
/other_embeddings/tw2v.py
- transformed word2vec (Kulkarni, Al-Rfou, Perozzi, Skiena 2015)
/visualization
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scripts for visualizations in paper
/visualization/norm_plots.py
- changepoint detection figures
/visualization/tsne_of_results.py
- trajectory figures
/distorted_smallNYT
- code for robust experiment
- data file download: https://www.dropbox.com/s/6q5jhhmxdmc8n1e/data.zip?dl=0
/misc
- contains general statistics and word hash file