Thousands of videos are constantly being uploaded to the web, creating a vast resource, and an ever-growing demand for methods to make them easier to retrieve, search, and index. As it becomes feasible to extract both low-level as well as high-level (symbolic) audio, speech, and video features from this data, these need to be processed further, in order to learn and extract meaningful relations between these. The language processing community has made huge process in analyzing the vast amounts of very noisy text data that is available on the Internet. While it is very difficult to create semantic units of low-level image descriptors or non-speech sounds by themselves, it is comparatively easy to ground semantics in the word output of a speech recognizer, or text data that is loosely associated with a video. This creates an opportunity for NLP researchers to use their unique skills, and make significant contributions to solve tasks on data that is even noisier than web text, but (we argue) even more interesting and challenging.
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