Konferenzbeitrag
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.
- Language
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Englisch
- Subject
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Computerlinguistik
Korpus <Linguistik>
Text Mining
Maschinelles Lernen
Lebensmittel
Sprache
- Event
-
Geistige Schöpfung
- (who)
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Wiegand, Michael
Roth, Benjamin
Klakow, Dietrich
- Event
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Veröffentlichung
- (who)
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Stroudsburg, PA : Association for Computational Linguistics
- (when)
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2019-02-05
- URN
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urn:nbn:de:bsz:mh39-84696
- Last update
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06.03.2025, 9:00 AM CET
Data provider
This object is provided by:
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Wiegand, Michael
- Roth, Benjamin
- Klakow, Dietrich
- Stroudsburg, PA : Association for Computational Linguistics
Time of origin
- 2019-02-05
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