Compared with the GSM model, this model can hugely alleviate the KL collapse problem and obtain more coherent topics. According to the original paper, the prior distribution of the latent vectors z is set as Dirichlet distribution, while the variational distribution is regulated under the Wasserstein distance. The architecture is a WAE, which is actually a straightforward AutoEncoder, with an additional regulation on the latent space. WAE with Dirichlet prior + Gaussian Softmax Original paper: Topic Modeling with Wasserstein AutoencodersĪuthor: Feng Nan, Ran Ding, Ramesh Nallapati, Bing Xiang $ python3 GSM_run.py -taskname cnews10k -n_topic 20 -num_epochs 1000 -no_above 0.0134 -no_below 5 -criterion cross_entropy -bkpt_continue: once adopted, the model will load the last checkpoint file and continue training. -auto_adj: once adopted, there would be no need to specify the no_above argument, the model will automatically filter out the top 20 words with the highest document frequencies. -no_above: to filter out the tokens whose document frequency is higher than the threshold, set as a float number to indicate the ratio of the number of documents. -no_below: to filter out the tokens whose document frequency is below the threshold, should be integer. -num_epochs: number of training epochs. -taskname: the name of the dataset, on which you want to build the topic model. The configuration of the encoder and decoder could also be customized by yourself, depending on your application. After sampling the latent vector z from the variational distribution Q(z|x), the model will normalize z through a softmax layer, which will be taken as the topic distribution $ \theta $ in the following steps. The architecture of the model is a simple VAE, which takes the BOW of a document as its input. Original paper: Discovering Discrete Latent Topics with Neural Variational Inference
Mmd bow install#
$ sudo pip install -r requirements.txt 2. Note: If you find it's slow to load the pictures of this readme file, you can read this article at my blog. If you have any question or suggestion about this implementation, please do not hesitate to contact me. As a comparison to the NTM, an out-of-box LDA script is also provided, which is based on the gensim library. Datasets of short news ( cnews10k), dialogue utterances ( zhddline) and conversation ( zhdd), are presented for evaluation purpose, all of which are in Chinese. Configuration of the models will not exactly the same as those proposed in the papers, and the hyper-parameters are not carefully finetuned, but I have chosen to get the core ideas covered.Įmpirically, NTM is superior to classical statistical topic models ,especially on short texts. The aim of this project is to provide a practical and working example for neural topic models to facilitate the research of related fields. All of the entries have been written and approved by actual scholars, which means you won’t have a problem when it comes time to cite sources. In addition to our massive store of reference material for fellow mariners, we are providing online bookings for various Maritime courses in maritime institutes across India.PyTorch implementations of Neural Topic Model varieties proposed in recent years, including NVDM-GSM, WTM-MMD (W-LDA), WTM-GMM, ETM, BATM ,and GMNTM.
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