https://github.com/Ward-nju/Topic-Model
https://github.com/Ward-nju/Topic-Model
machine-learning nlp
Last synced: about 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/Ward-nju/Topic-Model
- Owner: Ward-nju
- Created: 2017-06-13T10:18:07.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-10-02T10:30:31.000Z (over 8 years ago)
- Last Synced: 2024-11-18T19:41:42.083Z (over 1 year ago)
- Topics: machine-learning, nlp
- Language: Python
- Size: 28.3 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-topic-models - Topic-Model - Python implementation of LDA, Labeled LDA, ATM, Temporal Author-Topic Model using Gibbs sampling (Learning Implementations (hopefully easy to understand) / Embedding based Topic Models)
README
# Topic-Model
#### 2017.06.13
第一次用Github，一边学习一边更新，其实网上关于这部分的代码很多，但是自己写能加深认识和理解。
目前完成了
LDA: `Latent Dirichlet Allocation` by David M.Blei, et al. (2003)
Labeled-LDA: `Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora` by Ramage D, et al. (2009)
Author-Topic-Model: `The Author-Topic Model for Authors and Documents` by Rosen-Zvi, et al. (2004)
都是采用Gibbs Sampling实现。
接下来的任务：
1.代码里只设置了最大迭代次数，如何判断马尔科夫链的收敛，计算公式？
2.主题数确定：perplexity p(w|T)如何计算
3.推断
#### 2017.09.16
好久没看主题模型这方面的了，根据Boss要求，更新了一个主题模型：
`Exploiting Temporal Authors Interests via Temporal-Author-Topic Modeling` by Ali Daud, et al. (2009)
虽然代码可读性有点小瑕疵，不过经过试验，代码能完成基本的任务，暂时先这样吧~
#### 2017.09.19
去掉了Temporal-Author-Topic Modeling中的一个循环，并采用numba的@jit加速，效率有显著提升。
其他的准备也据此修改。