Topic modeling of textual corpora is an important and challenging problem. In most previous work, the "bag-of-words" assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.
Recently, there are several undirected graphical models being proposed, which typically outperform LDA. mcauliffe2008supervised present a two-layer undirected graphical model, called Replicated Softmax'', that can be used to model and automatically extract low-dimensional latent semantic representations from a large unstructured collection of document. hinton2009replicated extend Replicated Softmax'' by adding another layer of hidden units on top of the first with bipartite undirected connections. Neural network based approaches, such as Neural Autoregressive Density Estimators (DocNADE) @cite_2 and Hybrid Neural Network-Latent Topic Model @cite_27 , are also shown outperforming the LDA model.
@cite_27 @cite_2
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This paper introduces a hybrid model that combines a neural network with a latent topic model. The neural network provides a lowdimensional embedding for the input data, whose subsequent distribution is captured by the topic model. The neural network thus acts as a trainable feature extractor while the topic model captures the group structure of the data. Following an initial pretraining phase to separately initialize each part of the model, a unified training scheme is introduced that allows for discriminative training of the entire model. The approach is evaluated on visual data in scene classification task, where the hybrid model is shown to outperform models based solely on neural networks or topic models, as well as other baseline methods. We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical model of word counts that was shown to learn a better generative model and more meaningful document representations. Specifically, we take inspiration from the conditional mean-field recursive equations of the Replicated Softmax in order to define a neural network architecture that estimates the probability of observing a new word in a given document given the previously observed words. This paradigm also allows us to replace the expensive softmax distribution over words with a hierarchical distribution over paths in a binary tree of words. The end result is a model whose training complexity scales logarithmically with the vocabulary size instead of linearly as in the Replicated Softmax. Our experiments show that our model is competitive both as a generative model of documents and as a document representation learning algorithm.