Combining Thesaurus Knowledge and Probabilistic Topic Models

In this paper we present the approach of introducing thesaurus knowledge into probabilistic topic models. The main idea of the approach is based on the assumption that the frequencies of semantically related words and phrases, which are met in the same texts, should be enhanced: this action leads to their larger contribution into topics found in these texts. We have conducted experiments with several thesauri and found that for improving topic models, it is useful to utilize domain-specific knowledge. If a general thesaurus, such as WordNet, is used, the thesaurus-based improvement of topic models can be achieved with excluding hyponymy relations in combined topic models.Comment: Accepted to AIST-2017 conference (http://aistconf.ru/). The final publication will be available at link.springer.co

Stance Detection in Web and Social Media: A Comparative Study

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Social Power in Interactions: Computational Analysis and Detection of Power Relations

In this thesis, I investigate whether social power relations are manifested in the language and structure of social interactions, and if so, in what ways, and whether we can use the insights gained from this study to build computational systems that can automatically identify these power relations by analyzing social interactions. To further understand these manifestations, I extend this study in two ways. First, I investigate whether a person’s gender and the gender makeup of an interaction (e.g., are most participants female?) affect the manifestations of his/her power (or lack of it) and whether it can help improve the predictive performance of an automatic power prediction system. Second, I investigate whether different types of power manifest differently in interactions, and whether they exhibit different but predictable patterns. I perform this study on interactions from two different genres: organizational emails, which contain task oriented written interactions, and political debates, which contain discursive spoken interactions