@inproceedings{WiegandKlakow2011,
  author    = {Wiegand, Michael and Klakow, Dietrich},
  title     = {The Role of Predicates in Opinion Holder Extraction},
  series = {Proceedings of the RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition,16 September, 2011, Hissar, Bulgaria},
  publisher = {Incoma Ltd.},
  address   = {Shoumen},
  isbn      = {978-954-452-018-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-84564},
  pages     = {13 -- 20},
  year      = {2011},
  abstract  = {In this paper, we investigate the role of predicates in opinion holder extraction. We will examine the shape of these predicates, investigate what relationship they bear towards opinion holders, determine what resources are potentially useful for acquiring them, and point out limitations of an opinion holder extraction system based on these predicates. For this study, we will carry out an evaluation on a corpus annotated with opinion holders. Our insights are, in particular, important for situations in which no labelled training data are available and only rule-based methods can be applied.},
  language  = {en}
}
@inproceedings{WiegandKlakow2011,
  author    = {Wiegand, Michael and Klakow, Dietrich},
  title     = {Prototypical Opinion Holders: What We can Learn from Experts and Analysts},
  series = {Proceedings of the International Conference on Recent Advances in Natural Language Processing 2011, Hissar, Bulgaria, 12-14 September, 2011},
  editor    = {Angelova, Galia and Bontcheva, Kalina and Mitkov, Ruslan and Nikolov, Nikolai},
  publisher = {Incoma Ltd.},
  address   = {Shoumen},
  issn      = {1313-8502},
  url       = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-84674},
  pages     = {282 -- 288},
  year      = {2011},
  abstract  = {In order to automatically extract opinion holders, we propose to harness the contexts of prototypical opinion holders, i.e. common nouns, such as experts or analysts, that describe particular groups of people whose profession or occupation is to form and express opinions towards specific items. We assess their effectiveness in supervised learning where these contexts are regarded as labelled training data and in rule-based classification which uses predicates that frequently co-occur with mentions of the prototypical opinion holders. Finally, we also examine in how far knowledge gained from these contexts can compensate the lack of large amounts of labeled training data in supervised learning by considering various amounts of actually labeled training sets.},
  language  = {en}
}