Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
Michael Wiegand, Benjamin Roth, Dietrich Klakow · 2014
We present a weakly-supervised induction method to assign semantic information to food items.We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not.The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus.We show that the usage of a domain-specific corpus is vital.We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.