The implementation of the GPU-based Dirichlet Multinomial Mixture model (GPU-DMM), as described in 2016 SIGIR paper:
Topic Modeling for Short Texts with Auxiliary Word Embeddings. Haoran Wang, Chenliang Li, Zhiqian Zhang, Aixin Sun, Zongyang Ma.
GPU-DMM is a simple, fast and effective topic model for short texts.
This repository doesn't contain the preprocess steps. So if you want to use this code, you should prepare the data by yourself.
Also this repository doesn't contain the metric code for classification and PMI score.
The classification algorithm we used is SVM provided by scikit.
The PMI Coherence should calculated in external corpus, such as Wikipedia for English or Baidu Baike for Chinese.
The data format is described as follows:
docID \t category | content
example:
0 business|manufacture manufacturers suppliers supplier china directory products taiwan manufacturer
Anonther file you should prepare is the word similarity file. In our paper, we use the cosine similarity calculated on word embeddings. This can be prepared in advance.
beta: the hyper-parameter beta, and the alpha is calculated as 50/numTopic.
similarityFileName: the file of words' similarity
weight: the promotion of similar word
threshold: the similar threshold for constructing similar word set
filterSize: the filter size for filtering similar word set
numIter: the number of iteration for gibbs sampling progress
*_pdz.txt: the topic-level representation for each document. Each line is a topic distribution for one document. This is used for classification task.
*_phi.txt: the word-level representation for each topic. Each line is a word distribution for one topic. This is used for PMI Coherence task.
*_words.txt: word, wordID map information. This is used for PMI Coherence task.