C(7) semi-groups are finitely presented semi-groups were each relation decomposes into at least seven pieces. We give an example showing that, unlike for C(7) groups, the set of geodesics under some generating set is not an automatic structure (furthermore, even just a subset of the geodesics is not an automatic structure). We then show that under further restrictions, which imply embeddability...

The imbalanced sentiment distribution of microblogs induces bad performance of binary classifiers on the minority class. To address this problem, we present a semisupervised method for sentiment classification of Chinese microblogs. This method is similar to self-training, except that, a set of labeled samples is reserved for a confidence scores computing process through which samples that are ...

This article describes a method which allows acquiring artifact nouns in French automatically by extracting predicateargument structures. Two strategies are presented: the supervised strategy and the semi-supervised strategy. In the supervised method, the semantic classes of artifact nouns are recognized by identifying the predicate-argument structures with the syntactic patterns of the given p...

This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame. One of the main challenges in this scenario is the change of appearance of the objects of interest. Their semantics, on the other hand, do not vary. This paper investigates how to take advantage of such invariance via the introduction of a s...

Semi-static trading strategies make frequent appearances in mathematical finance, where dynamic trading in a liquid asset is combined with static buy-and-hold positions in options on that asset. We show that the space of outcomes of such strategies can have very poor closure properties when all European options for a fixed date T are available for static trading. This causes problems for optima...

We introduce a new model for building conditional generative models in a semisupervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the conditional distribution of the attributes given the data respectively. In the s...

Preference: Oral presentation

Semi supervised methods involve converting unlabelled data into high quality labelled data that can be used to improve the performance of conventional supervised methods that had previously been given a small training set. Unlabelled data has also been shown to be helpful in a supervised setting called ‘bridging’ where unlabelled data have been used to help relate labelled instances to those th...