In this paper, we investigate a supervised machine learning framework for automatically learning of English Light Verb Constructions (LVCs). Our system achieves an 86.3% accuracy with a baseline (chance) performance of 52.2% when trained with groups of either contextual or statistical features. In addition, we present an in-depth analysis of these contextual and statistical features and show th...

We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata abo...

Since synonyms are important lexical knowledge, various methods have been proposed for automatic synonym acquisition. Whereas most of the methods are based on the distributional hypothesis and utilize contextual clues, little attention has been paid to what kind of contextual information is useful for the purpose. As one of the ways to augment contextual information, we propose the use of indir...

In this paper two studies are presented in which the detection of speech recognition errors on the word level was examined. In the first study, memory-based and transformation-based machine learning was used for the task, using confidence, lexical, contextual and discourse features. In the second study, we investigated which factors humans benefit from when detecting errors. Information from th...

In this study, we propose an innovative approach using the “Contextual Mobile Learning System” based on the “Electronic Performance Support System” (EPSS) to support efficient just-in-time learning for professionals working in the “Smart city”. In this paper, we present the principle and the structure of our contextual mobile learning system, which uses a search engine to find appropriate learn...

The focus of this research is on the relationships between leader-member exchange (LMX), supportive supervisor communication (SSC), and subordinate job performance. It was predicted that the relationship between subordinate ratings of LMX quality and supervisor ratings of subordinate performance would be mediated by subordinate ratings of SSC. Specifically, it was hypothesized that LMX would di...

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have better empirical performance compared to the stateof-the-art methods. However, many questions regarding its theoretical performance remained open. In this paper, we d...

Automatically detecting verbal irony (roughly, sarcasm) in online content is important for many practical applications (e.g., sentiment detection), but it is difficult. Previous approaches have relied predominantly on signal gleaned from word counts and grammatical cues. But such approaches fail to exploit the context in which comments are embedded. We thus propose a novel strategy for verbal i...

Digital image collection interface layouts vary in the nature and degree of contextual information they provide to their users, thus enabling or impeding specific tasks. We are exploring image presentation techniques to support image-centric cognitive tasks in the context of cardiovascular systems research and education. To investigate the effect of image layout on user performance, we conducte...