With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, which are difficult to obtain in resource-scarce languages. To overcome this problem, we investigate the usefulness of label propagation, which is a graph-based semi-supervised learning method....

A novel reranking method has been developed to refine web search queries. A label propagation algorithm was applied on a clickthrough graph, and the candidates were reranked using a query language model. Our method first enumerates query candidates with common landing pages with regard to the given query to create a clickthrough graph. Second, it calculates the likelihood of the candidates, usi...

Most relational classifiers utilize some sort of label propagation for iterative inference. In this paper we differentiate between “hard” and “soft” label propagation. The latter method assigns probabilities or class-membership scores to data instances, then propagates these scores throughout the networked data, whereas the former works by explicitly propagating class labels at each iteration. ...

This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on th...

Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by ...

Recognizing speech act types in Twitter is of much theoretical interest and practical use. Our previous research did not adequately address the deficiency of training data for this multi-class learning task. In this work, we set out by assuming only a small seed training set and experiment with two semi-supervised learning schemes, transductive SVM and graph-based label propagation, which can l...

Multi-label classification is a challenging and appealing supervised learning problem where a subset of labels, rather than a single label seen in traditional classification problems, is assigned to a single test instance. Classifier chains based methods are a promising strategy to tackle multi-label classification problems as they model label correlations at acceptable complexity. However, the...

A large family of graph-based semi-supervised algorithms have been developed intuitively and pragmatically for the multi-label learning problem. These methods, however, only implicitly exploited the label correlation, as either part of graph weight or an additional constraint, to improve overall classification performance. Despite their seemingly quite different formulations, we show that all e...

Money-Laundering causes a higher prevalence of crime and reduces the desire tending to invest in productive activities. Also, it leads to weaken the integrity of financial markets and decrease government control over economic policy. Banks are able to prevent theft, fraud, money laundering conducted by customers through identification of their clients’ behavioral characteristics. This leads to ...

Recently label propagation recommendation receives much attention from both industrial and academic fields due to its low requirement of labeled training data and effective prediction. Previous methods propagate preferences on a user or item similarity graph for making recommendation. However, they still suffer some major problems, including data sparsity, lack of trustworthiness, cold-start pr...