Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emoti...

Considerable progress was recently made on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabeled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper suggests a space of basis functions to perform semi-supervised inductive learning. As a nice pr...

In sentiment classification, conventional supervised approaches heavily rely on a large amount of linguistic resources, which are costly to obtain for under-resourced languages. To overcome this scarce resource problem, there exist several methods that exploit graph-based semisupervised learning (SSL). However, fundamental issues such as controlling label propagation, choosing the initial seeds...

We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised learning. IPM-based GANs like Wasserstein GAN, Fisher GAN and Sobolev GAN have desirable properties in terms of theoretical understanding, training stability, and a meaningful loss. In this work we investigate how th...

We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a “null category noise model” (NCNM) inspired by ordered categorical noise models. The noise model reflects an assumption that the data density is lower between the class-conditional densities. We illustrate our approach on a toy problem and present comparative ...

We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a welldefined training set. We present extensive experimental results, which demonstrate that the proposed framework significa...

We introduce a novel approach for detection of objects from aerial images at the level of pixels using semi-supervised learning. Buildings in aerial images are complex 3D objects which are represented by features of different modalities include visual information and 3D height data. Semi-supervised learning is a classification which additional unlabeled data can be used to improve accuracy. Thi...

For efficient diagnosis processes, the multitude of heterogeneous medical data requires seamless integration. In order to automatically align radiology reports and images based on the pathological anatomical entities they describe, a preceding sentence classification is necessary. However, the lexical resource used has to contain semantic information about the pathological classification of eac...

The Universum sample, which is defined as the sample that doesn’t belong to any of the classes the learning task concerns, has been proved to be helpful in both supervised and semi-supervised settings. The former works treat the Universum samples equally. Our research found that not all the Universum samples are helpful, and we propose a method to pick the informative ones, i.e., inbetween Univ...