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资源说明：Practice of LDA
# About
Practice of LDA and other Topic Model based Collapsed Gibbs Sampling.
# todo
- Normal LDA
- Dynamic Topic Model
## Implementation
- Michal Rosen-Zvi, Tom Griffiths, Mark Steyvers, Padhraic Smyth. The Author-Topic Model for Authors and Documents. UAI (2004)
- Yi Fang, Luo Si, Naveen Somasundaram, Zhengtao Yu. Mining contrastive opinions on political texts using cross-perspective topic model. WSDM (2012)
- Daniel Ramage, David Hall, Ramesh Nallapati, Christopher D. Manning. Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. EMNLP (2009)
## Requirements
- g++ ( >= 4.6)
- boost
## Author-Topic Model
### usage
make author_topic
./author_topic input.tsv alpha t iter lim
### params
- input.tsv: input file(separated \t and :). Each line is document.
- format: author:author:...:author \t word:word:word ... :word
- alpha: \alpha.
- \beta if fix(= 0.01)
- t: # of topics.
- iter: # of gibbs sampling iterations.
- lim: limit of outputs.
## Cross-Perspective Topic Model
### usage
make cpt
./cpt per1_topic.tsv per1_opinion.tsv per2_topic.tsv per2_opinion.tsv alpha beta beta1 beta2 k iter lim
### params
- per1\_topic, per2\_topic: input topic file. Each line is document.
- format: word \t word ...
- per1\_opinion, per2\_opinion: input opinion file. Each line is document.
- format: word \t word ...
- topic/opinion file must same size.
- alpha: \alpha.
- beta, beta1, beta2: \beta.
- k: # of topics.
- iter: # of sampling iterations.
- lim: limit of outputs.
## Labeled LDA
### usage
make labeled_lda
./labeled_lda document_word_file document_label_file alpha iter lim
### params
- document\_word\_file: input doc-word file. Each line is document.
- format: document_name \t word \t word ...
- document\_label_file: input doc-label file. Each line is document.
- format: document_name \t label\_1 \t label\_2 ...
- doc-word/doc-label file must same size.
- alpha: \alpha.
- iter: # of sampling iterations.
- lim: limit of outputs.
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