Aspect extraction is a central problem in sentiment analysis. Current methods either extract aspects without categorizing them, or extract and categorize them using unsupervised topic modeling. By categorizing, we mean the synonymous aspects should be clustered into the same category. In this paper, we solve the problem in a different setting where the user provides some seed words for a few as...

The Web is the greatest information source in human history. Unfortunately, mining knowledge out of this source is a laborious and errorprone task. Many researchers believe that a solution to the problem can be founded on semantic annotations that need to be inserted in web-based documents and guide information extraction and knowledge mining. In this paper, we further elaborate a tool-supporte...

Semi-supervised learning is proposed to exploit both labeled and unlabeled data. However, as the scale of data in real world applications increases significantly, conventional semisupervised algorithms usually lead to massive computational cost and cannot be applied to large scale datasets. In addition, label noise is usually present in the practical applications due to human annotation, which ...

Sequential sensor data is generated in a wide variety of real-world applications. A fundamental machine learning challenge involves effective classifiers for such sequential data. While deep has led to impressive performance gains recent years within domains as speech, this relied on the availability large datasets sequences with high-quality labels. In many applications, however, associated cl...