@inproceedings{WiegandRothKlakow2019,
  author    = {Wiegand, Michael and Roth, Benjamin and Klakow, Dietrich},
  title     = {Data-driven Knowledge Extraction for the Food Domain},
  booktitle = {Proceedings of the 11th Conference on Natural Language Processing (KONVENS 2012). Empirical Methods in Natural Language Processing, September 19-21, 2012, Vienna, Austria},
  editor    = {Jancsary, Jeremy},
  isbn      = {3-85027-005-X},
  url       = {http://www.oegai.at/konvens2012/proceedings.shtml},
  series    = {Schriftenreihe der {\"O}sterreichischen Gesellschaft f{\"u}r Artificial Intelligence ({\"O}GAI)},
  number    = {Band 5},
  pages     = {21 -- 29},
  year      = {2019},
  abstract  = {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.},
  subject      = {Information Extraction},
  language  = {en}
}