Modeling Noisy Annotated Data with Application to Social Annotation
Abstract— Modeling Noisy Annotated Data with Application to Social Annotation. We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as webpages stored using social bookmarking services. With these services, because users can attach annotations freely, some annotations do not describe the semantics of the content, thus they are noisy, i.e., not content related. The extraction of content-related annotations can be used as a prepossessing step in machine learning tasks such as text classification and image recognition, or can improve information retrieval performance. The proposed model is a generative model for content and annotations, < Final Year Projects > in which the annotations are assumed to originate either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method by using synthetic data and real social annotation data for text and images.
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