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Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction

  • We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.

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Metadaten
Author:Michael WiegandGND, Benjamin Roth, Dietrich Klakow
URN:urn:nbn:de:bsz:mh39-84696
URL:https://aclanthology.info/papers/E14-1071/e14-1071
DOI:https://doi.org/10.3115/v1/E14-1071
ISBN:978-1-937284-78-7
Parent Title (English):Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, April 26-30, 2014, Gothenburg, Sweden
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Document Type:Conference Proceeding
Language:English
Year of first Publication:2014
Date of Publication (online):2019/02/05
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
GND Keyword:Computerlinguistik; Korpus <Linguistik>; Lebensmittel; Maschinelles Lernen; Text Mining
First Page:673
Last Page:682
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution-NonCommercial-ShareAlike 3.0 Unported