Utilities related to D2 Clustering for Document Data
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.
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.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
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:
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