Konferenzbeitrag

Data-driven Knowledge Extraction for the Food Domain

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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.

Data-driven Knowledge Extraction for the Food Domain

Urheber*in: Wiegand, Michael; Roth, Benjamin; Klakow, Dietrich

In copyright

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Language
Englisch

Subject
Information Extraction
Computerlinguistik
Korpus <Linguistik>
Empirische Linguistik
Lebensmittel
Sprache

Event
Geistige Schöpfung
(who)
Wiegand, Michael
Roth, Benjamin
Klakow, Dietrich
Event
Veröffentlichung
(who)
Wien : Österreichische Gesellschaft für Artificial Intelligence
(when)
2019-01-28

URN
urn:nbn:de:bsz:mh39-84529
Last update
06.03.2025, 9:00 AM CET

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Object type

  • Konferenzbeitrag

Associated

  • Wiegand, Michael
  • Roth, Benjamin
  • Klakow, Dietrich
  • Wien : Österreichische Gesellschaft für Artificial Intelligence

Time of origin

  • 2019-01-28

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