In this paper, we propose to study the problem of identifying and classifying tweets into intent categories. For example, a tweet “I wanna buy a new car” indicates the user’s intent for buying a car. Identifying such intent tweets will have great commercial value among others. In particular, it is important that we can distinguish different types of intent tweets. We propose to classify intent ...

This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show t...

Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs. A powerful...

We propose two transductive bounds on the risk of majority votes that are estimated over partially labeled training sets. The first one involves the margin distribution of the classifier and a risk bound on its associate Gibbs classifier. The bound is tight when so is the Gibbs’s bound and when the errors of the majority vote classifier is concentrated on a zone of low margin. In semi-supervise...

Stance detection aims to automatically identify if the text author is in favor or against a subject or target. This work describes a semi-supervised method for stance detection. The core is a set of rules to identify stance based on positive or negative opinions of targets directly or indirectly related. Tweets automatically labeled using the rules compose a training corpus for a supervised app...

In this paper, we propose a novel graph-based method for semi-supervised learning. Our method runs a diffusion-based affinity learning algorithm on an augmented graph consisting of not only the nodes of labeled and unlabeled data but also artificial nodes representing class labels. The learned affinities between unlabeled data and class labels are used for classification. Our method achieves su...

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simulta...

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential ...

The harmonic solution (HS) on a graph is one of the most popular approaches to semi-supervised learning. This is the first paper that studies how to identify highly confident HS predictions on a graph based on the HS on its subgraph. The premise of our method is that the subgraph is much smaller than the graph and therefore the most confident predictions can be identified much faster than compu...

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