Document Sub-structure in Neural Machine Translation

a) First of all, we want to provide the community with a new corpus exploration method able to produce topics that are easier to interpret than standard LDA topic models. We do so

More precisely, given the topic proportions, a document is generated with our model by selecting a subset of diverse sentences among all the possible sentences and the

The pLSI approach, which we describe in detail in Section 4.3, models each word in a document as a sample from a mixture model, where the mixture components are multinomial

The key idea for parallelizing the sampler in the multicore setting is that the global topic distribution and the topic- word table (which we will refer to as state of the

In order to extract the distribution of word senses over time and positions, we use topic models that consider temporal information about the documents as well as locations in form

Topic models, implemented using Latent Dirichlet Allocation (LDA) [2], and n–gram language models were used to extract features to train Support Vector Machine (SVM) classifiers

Our preliminary results are obtained us- ing a methodology that pulls strengths from several machine learning techniques, including Latent Dirichlet Allocation (LDA) for topic

The basic models, PLSA and LDA are designed for one-type data and advanced models are designed for modeling multi-type data, especially for image modeling and