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

ACL ID C14-1216
Title Separating Brands from Types: an Investigation of Different Features for the Food Domain
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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 Wik...