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Poster
Modeling Social Annotation Data with Content Relevance using a Topic Model
Tomoharu Iwata · Takeshi Yamada · Naonori Ueda

Mon Dec 07 07:00 PM -- 11:59 PM (PST) @ None #None

We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as web pages stored in social bookmarking services. In these services, since 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 preprocessing 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, 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.

Author Information

Tomoharu Iwata (NTT)
Takeshi Yamada (NTT Communication Science Labs.)
Naonori Ueda (NTT Communication Science Laboratories)

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