Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. Determining the optimal number of topics remains a challenging problem in topic modeling. We propose a simple entropy regularization for topic selection in terms of Additive Regularization of Topic Models (ARTM), a multicriteria approach for combining regularizers. The entropy regularization gradu...

Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic ...

We investigate the idea of using a topic model such as the popular Latent Dirichlet Allocation model as a feature selection step for unsupervised document clustering, where documents are clustered using the proportion of the various topics that are present in each document. One concern with using “vanilla” LDA as a feature selection method for input to a clustering algorithm is that the Dirichl...

This paper presents an application of nonlinear neural networks to topic spotting. Neural networks allow us to model higher-order interaction between document terms and to simultaneously predict multiple topics using shared hidden features. In the context of this model, we compare two approaches to dimensionality reduction in representation: one based on term selection and another based on Late...

This vignette demonstrates how to use the Structural Topic Model, stm, R package. The Structural Topic Model (STM) allows researchers to estimate a topic model which includes document-level meta-data. The stm package provides a range of features from model selection to extensive plotting and visualization options.

We perform a comprehensive examination of the recently proposed anchor method for topic model inference using topic interpretability and held-out likelihood measures. After measuring the sensitivity to the anchor selection process, we incorporate L2 and Beta regularization into the optimization objective in the recovery step. Preliminary results show that L2 improves heldout likelihood, and Bet...

This paper introduces a selection-based LM using topic modeling for the purpose of domain adaptation which is often required in Statistical Machine Translation. The performance of this selection-based LM slightly outperforms the state-of-theart Moore-Lewis LM by 1.0% for EN-ES and 0.7% for ES-EN in terms of BLEU. The performance gain in terms of perplexity was 8% over the Moore-Lewis LM and 17%...

Information retrieval from XML documents offers an opportunity to go below the document level in search of relevant information, making any element of an XML document a retrievable unit. We consider two dimensions along which we compare this element retrieval task with the traditional document retrieval task. We investigate how different topic representations and language model smoothing approa...