TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: This paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system. For creating the links, we build on the Polylingual Labeled Topic Model (PLL-TM) [6]. PLL-TM identifies probable thesaurus descriptors for each class in the classification system by using information from the natural language text of documents, their assigned thesaurus descriptors and their designated classes. The links are then presented to users of the system in an interactive visualization, providing them with an automatically generated overview of the relations between the thesaurus and the classification system. Lisa Posch GESIS – Leibniz Institute for the Social Sciences Cologne, Germany Institute for Web Science and Technologies University of Koblenz-Landau, Germany E-mail: lisa.posch@gesis.org Philipp Schaer GESIS – Leibniz Institute for the Social Sciences Cologne, Germany E-mail: philipp.schaer@gesis.org Arnim Bleier GESIS – Leibniz Institute for the Social Sciences Cologne, Germany E-mail: arnim.bleier@gesis.org Markus Strohmaier GESIS – Leibniz Institute for the Social Sciences Cologne, Germany Institute for Web Science and Technologies University of Koblenz-Landau, Germany E-mail: markus.strohmaier@gesis.org

SOLUTION: A System for Probabilistic Linking of Thesauri and Classification Systems

PROBLEM: This paper is an empirical study of the distributed deep learning for a question answering subtask: answer selection. Comparison studies of SGD, MSGD, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the message passing interface based distributed framework can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training: with 120 workers, an 83x speedup is achievable and running time is decreased from 107.9 hours to 1.3 hours, which will benefit the productivity significantly.

SOLUTION: Distributed Deep Learning for Answer Selection

PROBLEM: Selecting the right reference class and the right interval when faced with conflicting candidates and no possibility of establish­ ing subset style dominance has been a prob­ lem for Kyburg's Evidential Probability sys­ tem. Various methods have been proposed by Loui and Kyburg to solve this problem in a way that is both intuitively appealing and justifiable within Kyburg's framework. The scheme proposed in this paper leads to stronger statistical assertions without sacri­ ficing too much of the intuitive appeal of Ky­ burg's latest proposal. 1 Overview of the Problem

SOLUTION:
A Modification to Evidential Probability