TY - CHAP U1 - Konferenzveröffentlichung A1 - Wiegand, Michael A1 - Klakow, Dietrich T1 - Convolution Kernels for Opinion Holder Extraction T2 - Proceedings of HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, June 2–4, 2010, Los Angeles, California N2 - Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary. KW - Computerlinguistik KW - Information Extraction KW - Meinung KW - Natürliche Sprache KW - Maschinelles Lernen KW - Sentimentanalyse Y1 - 2010 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84345 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84345 UR - https://dl.acm.org/citation.cfm?id=1858120 SN - 978-1-932432-65-7 SB - 978-1-932432-65-7 SP - 795 EP - 803 PB - Association for Computational Linguistics CY - Stroudsburg, PA ER -