Konferenzbeitrag
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.
- Sprache
-
Englisch
Roth, Benjamin
Klakow, Dietrich
- URN
-
urn:nbn:de:bsz:mh39-84529
- Letzte Aktualisierung
-
14.09.2023, 08:26 MESZ
Objekttyp
- Konferenzbeitrag
Beteiligte
- Wiegand, Michael
- Roth, Benjamin
- Klakow, Dietrich
- Wien : Österreichische Gesellschaft für Artificial Intelligence
Entstanden
- 2019-01-28
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