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.

We propose a novel fully-automated approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the is-a relation in the target language. Character-level classifiers are trained on the constructed datasets, and used in an optimal path discovery framework to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages.
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-level Classification

Class imbalance is one of the challenging problems for machine learning in many real-world applications, such as coal and gas burst accident monitoring: the burst premonition data is extreme smaller than the normal data, however, which is the highlight we truly focus on. Cost-sensitive adjustment approach is a typical algorithm-level method resisting the data set imbalance. For SVMs classifier, which is modified to incorporate varying penalty parameter(C) for each of considered groups of examples. However, the C value is determined empirically, or is calculated according to the evaluation metric, which need to be computed iteratively and time consuming. This paper presents a novel cost-sensitive SVM method whose penalty parameter C optimized on the basis of cluster probability density function(PDF) and the cluster PDF is estimated only according to similarity matrix and some predefined hyper-parameters. Experimental results on various standard benchmark data sets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used cost-sensitive techniques.
Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification

A key problem in sensor networks is to decide which sensors<lb>to query when, in order to obtain the most useful information<lb>(e.g., for performing accurate prediction), subject to con-<lb>straints (e.g., on power and bandwidth). In many applications<lb>the utility function is not known a priori, must be learned<lb>from data, and can even change over time. Furthermore for<lb>large sensor networks solving a centralized optimization prob-<lb>lem to select sensors is not feasible, and thus we seek a fully<lb>distributed solution. In this paper, we present Distributed<lb>Online Greedy (DOG), an efficient, distributed algorithm for<lb>repeatedly selecting sensors online, only receiving feedback<lb>about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (un-<lb>known) utility function satisfies a natural diminishing returns<lb>property called submodularity. Our algorithm has extremely<lb>low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-<lb>dependent sensor selection. We empirically demonstrate the<lb>effectiveness of our algorithm on several real-world sensing<lb>tasks.
Online Distributed Sensor Selection