Supervised topic models utilize document's side information for discovering predictive low
dimensional representations of documents; and existing models apply likelihood-based …

We describe the use of a hierarchical topic model for automatically identifying syntactic and
lexical patterns that explicitly state ontological relations. We leverage distant supervision …

Transfer learning has shown promising results for transferring knowledge ofsource tasks to
target tasks in natural language processing (NLP). In this paper, we investigate a multi-task …

Deep learning models have achieved great successes these days. There are intensive
studies of word representation learning for question classification. As questions are typically …

An effective strategy to exploit the supervising side information for discovering predictive
topic representations is to impose discriminative constraints induced by such information on …

This article describes our participation of the Gene Ontology Curation task (GO task) in
BioCreative IV where we participated in both subtasks: A) identification of GO evidence …

The consensus algorithm is proposed to solve the bounded consensus tracking problems of
leader-following multi-agent systems under directed networks, where the control input of an …

In this paper, we propose a model called Temporal & Co reference Topic Modeling (TCTM)
to do automatic annotation with respect to the Time Event Ontology (TEO) for the big-size …

In intensive care units (ICUs), mortality prediction using vital sign or demographics of
patients yields helpful information to support the decision-making of intensivists. Clinical …

Analyzing motion patterns in traffic videos can be employed directly to generate high-level
descriptions of their content. For traffic videos captured from intersections, usually, we can …