Natural language processing applications typically need large amounts of information at the lexical level: words that are similar in meaning, idioms and collocations, typical relations between entities,lexical patterns that can be used to draw inferences, and so on. Today such information is mostly collected automatically from large amounts of data, making use of regularities in the co-occurrence of words. But documents often contain more than just co-occurring words, for example illustrations, geographic tags, or a link to a date. Just like co-occurrences between words, these co-occurrences of words and extra-linguistic data can be used to automatically collect information about meaning. The resulting grounded models of meaning link words to visual, geographic, or temporal information. Such models can be used in many ways: to associate documents with geographic locations or points in time, or to automatically find an appropriate image for a given document, or to generate text to accompany a given image.
In this seminar, we discuss different types of extra-linguistic data, and their use for the induction of grounded models of meaning.