TY - CHAP U1 - Konferenzveröffentlichung A1 - Wiegand, Michael A1 - Klakow, Dietrich T1 - Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction T2 - Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, April 23-27 2012, Avignon France N2 - In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction being simple unsupervised word clustering, an induction method inspired by distant supervision and the usage of lexical resources. The generalization methods are incorporated into diverse classifiers. We show that generalization causes significant improvements and that the impact of improvement depends on the type of classifier and on how much training and test data differ from each other. We also address the less common case of opinion holders being realized in patient position and suggest approaches including a novel (linguistically-informed) extraction method how to detect those opinion holders without labeled training data as standard datasets contain too few instances of this type. KW - Computerlinguistik KW - Information Extraction KW - Natürliche Sprache KW - Maschinelles Lernen KW - Meinung KW - Sentimentanalyse Y1 - 2012 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84378 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84378 UR - https://dl.acm.org/citation.cfm?id=2380857 SN - 978-1-937284-19-0 SB - 978-1-937284-19-0 SP - 325 EP - 335 PB - Association for Computational Linguistics CY - Stroudsburg, PA ER -