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Data-driven Knowledge Extraction for the Food Domain

  • In this paper, we examine methods to automatically extract domain-specific knowledge from the food domain from unlabeled natural language text. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. We also examine a combination of extraction methods and also consider relationships between different relation types. The extraction methods are applied both on a domain-specific corpus and the domain-independent factual knowledge base Wikipedia. Moreover, we examine an open-domain lexical ontology for suitability.

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Author:Michael WiegandGND, Benjamin Roth, Dietrich Klakow
Parent Title (English):Proceedings of the 11th Conference on Natural Language Processing (KONVENS 2012). Empirical Methods in Natural Language Processing, September 19-21, 2012, Vienna, Austria
Series (Serial Number):Schriftenreihe der Österreichischen Gesellschaft für Artificial Intelligence (ÖGAI) (Band 5)
Publisher:Österreichische Gesellschaft für Artificial Intelligence
Place of publication:Wien
Editor:Jeremy Jancsary
Document Type:Conference Proceeding
Year of first Publication:2012
Date of Publication (online):2019/01/28
GND Keyword:Computerlinguistik; Empirische Linguistik; Information Extraction; Korpus <Linguistik>; Lebensmittel
First Page:21
Last Page:29
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):License LogoUrheberrechtlich geschützt