No abstract

Copulas enable flexible parameterization of multivariate distributions in terms of constituent marginals and dependence families. Vine copulas, hierarchical collections of bivariate copulas, can model a wide variety of dependencies in multivariate data including asymmetric and tail dependencies which the more widely used Gaussian copulas, used in Meta-Gaussian distributions, cannot. However, current inference algorithms for vines cannot fit data with mixed-a combination of continuous, binary and ordinal-features that are common in many domains. We design a new inference algorithm to fit vines on mixed data thereby extending their use to several applications. We illustrate our algorithm by developing a dependency-seeking multi-view clustering model based on Dirichlet Process mixture of vines that generalizes previous models to arbitrary dependencies as well as to mixed marginals. Empirical results on synthetic and real datasets demonstrate the performance on clustering single-view and multi-view data with asymmetric and tail dependencies and with mixed marginals.

Abstract. The Hierarchical Dirichlet Process (HDP) is a Bayesian nonparametric prior for grouped data, such as collections of documents, where each group is a mixture of a set of shared mixture densities, or topics, where the number of topics is not fixed, but grows with data size. The Nested Dirichlet Process (NDP) builds on the HDP to cluster the documents, but allowing them to choose only from a set of specific topic mixtures. In many applications, such a set of topic mixtures may be identified with the set of entities for the collection. However, in many applications, multiple entities are associated with documents, and often the set of entities may also not be known completely in advance. In this paper, we address this problem using a nested HDP (nHDP), where the base distribution of an outer HDP is itself an HDP. The inner HDP creates a countably infinite set of topic mixtures and associates them with entities, while the outer HDP associates documents with these entities or topic mixtures. Making use of a nested Chinese Restaurant Franchise (nCRF) representation for the nested HDP, we propose a collapsed Gibbs sampling based inference algorithm for the model. Because of couplings between two HDP levels, scaling up is naturally a challenge for the inference algorithm. We propose an inference algorithm by extending the direct sampling scheme of the HDP to two levels. In our experiments on two real world research corpora, we show that, even when large fractions of author entities are hidden, the nHDP is able to generalize significantly better than existing models. More importantly, we are able to detect missing authors at a reasonable level of accuracy.

Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity. We address various components of this task: chunk level semantic alignment along with assignment of similarity type and score for aligned chunks with a novel system presented in this paper. We propose an algorithm, iMATCH, for the alignment of multiple non-contiguous chunks based on Integer Linear Programming (ILP). Similarity type and score assignment for pairs of chunks is done using a supervised multiclass classification technique based on Random Forrest Classifier. Results show that our algorithm iMATCH has low execution time and outperforms most other participating systems in terms of alignment score. Of the three datasets, we are top ranked for answerstudents dataset in terms of overall score and have top alignment score for headlines dataset in the gold chunks track.

No abstract