Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair ...

Adverse drug events (ADEs) have been recognized as a significant healthcare problem worldwide. Prior studies have shown that health social media can be used to generate medical hypotheses and identify adverse drug events. Most studies adopted supervised learning approach for ADE detection in health social media, which requires human annotated data and is not scalable to large datasets. In this ...

We propose a semi-supervised framework to handle diverse data formats or data with mixedtype attributes. Our preliminary results in clustering data with mixed numerical and categorical attributes show that the proposed semi-supervised framework gives better clustering results in the categorical domain. Thus the seeds obtained from clustering the numerical domain give an additional knowledge to ...

Object detection is an import task of computer vision. A variety of methods have been proposed, but methods using the weak labels still do not have a satisfactory result. In this paper, we propose a new framework that using the weakly supervised method’s output as the pseudo-strong labels to train a strongly supervised model. One weakly supervised method is treated as black-box to generate clas...

In this paper, we present a novel semi-supervised learning framework based on `1 graph. The `1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed `1 graph, are derived by solving an `1 optimization problem on sparse representation. Different from co...

In recent years, the study of classification shifted to algorithms for training the classifier from data that may be missing the class label. While traditional supervised classifiers already have the ability to cope with some incomplete data, the new type of classifiers do not view unlabeled data as an anomaly, and can learn from data sets in which the large majority of training points are unla...

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning...

We present an objective function for learning with unlabeled data that utilizes auxiliary expectation constraints. We optimize this objective function using a procedure that alternates between information and moment projections. Our method provides an alternate interpretation of the posterior regularization framework (Graca et al., 2008), maintains uncertainty during optimization unlike constra...

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...

Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current cluster...