Social tagging is an emerging methodology that allows individual users to assign semantic keywords to content on the web. Popular web services allow the community of users to search for content based on these user-defined tags. Tags are typically attached to a whole entity such as a web page (e.g., del.icio.us), a video (e.g., YouTube), a product description (e.g., Amazon) or a photograph (e.g., Flickr). However, finding specific information within a whole entity can be a difficult, time-intensive process. This is especially true for content such as video, where the information sought may be a small segment within a very long presentation. Moreover, the tags provided by a community of users may be incorrect, conflicting, or incomplete when used as search terms. In this paper we introduce a system that allows users to create "micro-tags," that is, semantic markers that are attached to subsets of information. These micro-tags give the user the ability to direct attention to specific subsets within a larger and more complex entity, and the set of micro-tags provides a more nuanced description of the full content. Also, when these micro-tags are used as search terms, there is no need to do a serial search of the content, since micro-tags draw attention to the semantic content of interest. This system also provides a mechanism that allows users in the community to edit and delete each others' tags, using the community to refine and improve tag quality. We will also report on empirical studies that demonstrate the value of micro-tagging and tag editing and explore the role micro-tags and tag editing will play in future applications.