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When browsing news on the web, various emotions may be evoked in readers and furthermore cause different influence on their minds and life. We expect that emotional analysis and classification of text may provide good performance and significance to users surfing the Internet. Most previous research only focus on bi-emotion classification, that is, Positive and Negative, e.g., identifying whether...
In real-world information systems, there are abundant unlabeled data but sparse labeled data. It is challenging to construct an adaptive model to classify a large amount of documents containing different domains. The classifiers trained from a source domain shall perform poorly for the test data in a target domain due to the domain mismatch. In this study, we build a topic-bridged latent Dirichlet...
The close visual relation between the style of typographic words in a document from one side and the conceptual meaning of `texture' from the other side, has been used to propose an approach based on gabor filter extracted features to classify words in a document into three classes of regular, italic and bold. Since the generalized dirichlet distribution (GDD) is shown to be very flexible in image...
In this paper we consider the problem of unsupervised separation of mixed text patterns based on blind source separation models. We propose a hierarchical Markov random field model for the source patterns, which enforces piece-wise regularity on both labels and intensities of image pixels. We also presented a hierarchical Bayesian BSS framework, in which the unknown sources and labels is estimated...
This paper presents a corpus-based approach for extracting keywords from a text written in a language that has no word boundary. Based on the concept of Thai character cluster, a Thai running text is preliminarily segmented into a sequence of inseparable units, called TCCs. To enable the handling of a large-scaled text, a sorted sistring (or suffix array) is applied to calculate a number of statistics...
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