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Separating Brands from Types: an Investigation of Different Features for the Food Domain

  • We examine the task of separating types from brands in the food domain. Framing the problem as a ranking task, we convert simple textual features extracted from a domain-specific corpus into a ranker without the need of labeled training data. Such method should rank brands (e.g. sprite) higher than types (e.g. lemonade). Apart from that, we also exploit knowledge induced by semi-supervised graph-based clustering for two different purposes. On the one hand, we produce an auxiliary categorization of food items according to the Food Guide Pyramid, and assume that a food item is a type when it belongs to a category unlikely to contain brands. On the other hand, we directly model the task of brand detection using seeds provided by the output of the textual ranking features. We also harness Wikipedia articles as an additional knowledge source.

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Author:Michael WiegandGND, Dietrich Klakow
Parent Title (English):Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, August 23-29, 2014, Dublin, Ireland: Technical Papers
Publisher:Dublin City University
Place of publication:Dublin
Document Type:Conference Proceeding
Year of first Publication:2014
Date of Publication (online):2019/02/13
GND Keyword:Computerlinguistik; Information Extraction; Lebensmittel; Maschinelles Lernen; Natürliche Sprache
First Page:2291
Last Page:2302
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (English):License LogoCreative Commons - Attribution 4.0 International