This repository includes python 2.7 scripts that process a document dataset file into .d2s format that is ready for applying software package d2_kmeans. The clustering result provided by d2_kmeans is then evaluated by different metrics. If you are interested in the software d2_kmeans and reproduce the results in the paper, please contact the author @JianboYe directly.
The utilities involved were used for generating part of the results reported in the following paper:
Jianbo Ye, Yanran Li, Zhaohui Wu, James Z. Wang, Wenjie Li, Jia Li, Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering, Proceedings of The Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, Canada, July 2017. Long paper.
Download sample datasets from the author's webpage.
$ wget http://infolab.stanford.edu/~wangz/project/linguistics/ACL17/acl2017dataset.zip
$ unzip acl2017dataset.zip
Download pre-trained wordvecs, two of which are public downloadable.
- glove_6B_300d.bin
- GoogleNews-vectors-negative300.bin
- word2vec_400_10_10.bin
Install python (version 2.7) and its dependencies. The tested versions are
- numpy (1.9.2)
- scipy (1.9.2)
- sklearn (0.16.1)
- cvxopt (1.1.7)
- gensim (0.12.1)
- nltk (3.0.5)
- mosek (optional, 7.x)
You may need adapt the code to newer versions if needed.
After you configure the python environment properly, you can start from a sample dataset, say story_cluster.txt, and a wordvec model, say glove_6B_300d.bin. The following command create d2s formated data from story_cluster.txt. Edit the source for adapting to other datasets.
$ python export_d2s.py
raw categories: 54
document count: 1983
average words: 22
(1983, 4849)
It creates two files: story_cluster.d2s and story_cluster.d2s.vocab0. At this point, you need to request a patent protected C/MPI software called d2_kmeans. The software will take these two files are input and output clustering labels as a file named story_cluster.d2s_[xxxxxx].label_o in the same directory. Type the same command again to evaluate the result that was reported in the paper.
$ python export_d2s.py
如果你想尝试重现文章中的结果，还可以看这篇文章。
The MIT License (MIT)
Copyright (c) 2017 Jianbo Ye
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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