Automatic personality analysis has gained attention in the last years as a fundamental dimension in human-to-human and human-to-machine interaction. However, it still suffers from limited number and size of speech corpora for specific domains, such as the assessment of children’s personality. This paper investigates a semi-supervised training approach to tackle this scenario. We devise an exper...

The subject matter of this paper concerns the numerical approximation of reduced Vlasov-Maxwell models by semi-Lagrangian schemes. Such reduced systems have been introduced recently in the literature for studying the laser-plasma interaction. We recall the main existence and uniqueness results on these topics, we present the semi-Lagrangian scheme and finally we establish the convergence of thi...

Tweets ranking is important for information acquisition in Microblog. Due to the content sparsity and lack of labeled data, it is better to employ semi-supervised learning methods to utilize the unlabeled data. However, most of previous semi-supervised learning methods do not consider the pair conflict problem, which means that the new selected unlabeled data may have order conflict with the la...

Most of previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models. However, creating such resources is an expensive and time-consuming task. In this paper, we exploit semi-supervised learning with the co-training algorithm f...

Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised learning algorithms are evaluated on 2...

The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly ...

Methods to analyze well-test data influenced by variable-skin and cleanup effects are introduced, with emphasis on verifications of consistency in drawdown and buildup data. It is shown that a stable cleanup trend can be modeled by a simple hyperbolic skin factor in solutions for wells with constant skin. It is also shown that if the skin effect stabilizes at constant values within the general ...

We describe a new approach to disambiguating semantic frames evoked by lexical predicates previously unseen in a lexicon or annotated data. Our approach makes use of large amounts of unlabeled data in a graph-based semi-supervised learning framework. We construct a large graph where vertices correspond to potential predicates and use label propagation to learn possible semantic frames for new o...

Unknown lexical items present a major obstacle to the development of broadcoverage semantic role labeling systems. We address this problem with a semisupervised learning approach which acquires training instances for unseen verbs from an unlabeled corpus. Our method relies on the hypothesis that unknown lexical items will be structurally and semantically similar to known items for which annotat...

Semi-Supervised Learning (SSL) techniques have become very relevant since they require a small set of labeled data. In this scenario, graph-based SSL algorithms provide a powerful framework for modeling manifold structures in high-dimensional spaces and are effective for the propagation of the few initial labels present in training data through the graph. An important step in graph-based SSL me...