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A Gold Standard for Relation Extraction in the Food Domain

  • We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.

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Author:Michael WiegandGND, Benjamin Roth, Eva LasarcykGND, Stephanie Köser, Dietrich Klakow
Parent Title (English):Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), May 21-27, 2012, Istanbul, Turkey
Publisher:European Language Resources Association
Place of publication:Paris
Editor:Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Document Type:Conference Proceeding
Year of first Publication:2012
Date of Publication (online):2019/01/24
Tag:Domain-specific Relation Extraction; Food Domain; Information Extraction
GND Keyword:Computerlinguistik; Information Extraction; Korpus <Linguistik>; Lebensmittel; Natürliche Sprache
First Page:507
Last Page:514
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (English):License LogoCreative Commons - Attribution-NonCommercial-ShareAlike 3.0 Unported