Konferenzbeitrag

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.

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

Urheber*in: Wiegand, Michael; Klakow, Dietrich

Attribution 4.0 International

0
/
0

Language
Englisch

Subject
Computerlinguistik
Natürliche Sprache
Information Extraction
Maschinelles Lernen
Lebensmittel
Sprache

Event
Geistige Schöpfung
(who)
Wiegand, Michael
Klakow, Dietrich
Event
Veröffentlichung
(who)
Dublin : Dublin City University
(when)
2019-02-13

URN
urn:nbn:de:bsz:mh39-84874
Last update
06.03.2025, 9:00 AM CET

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Object type

  • Konferenzbeitrag

Associated

  • Wiegand, Michael
  • Klakow, Dietrich
  • Dublin : Dublin City University

Time of origin

  • 2019-02-13

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