@inproceedings{WiegandRothKlakow2019, author = {Michael Wiegand and Benjamin Roth and Dietrich Klakow}, title = {Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction}, series = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, April 26-30, 2014, Gothenburg, Sweden}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg, PA}, isbn = {978-1-937284-78-7}, doi = {10.3115/v1/E14-1071}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84696}, pages = {673 -- 682}, year = {2019}, abstract = {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.}, language = {en} }