TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wiegand, Michael
A1  - Klakow, Dietrich
T1  - The Role of Predicates in Opinion Holder Extraction
T2  - Proceedings of the RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition,16 September, 2011, Hissar, Bulgaria
N2  - 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.
KW  - Information Extraction
KW  - Computerlinguistik
KW  - Prädikat
KW  - Maschinelles Lernen
KW  - Natürliche Sprache
KW  - Sentimentanalyse
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:mh39-84564
UN  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:mh39-84564
UR  - https://aclanthology.info/papers/W11-4004/w11-4004
SN  - 978-954-452-018-2
SB  - 978-954-452-018-2
SP  - 13
EP  - 20
PB  - Incoma Ltd.
CY  - Shoumen
ER  - 
TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wiegand, Michael
A1  - Klakow, Dietrich
ED  - Sandford Pedersen, Bolette
ED  - Nešpore, Gunta
ED  - Skadiņa, Inguna
T1  - Convolution Kernels for Subjectivity Detection
T2  - Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011), May 11-13, 2011, Riga, Latvia
N2  - In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.
T3  - NEALT Proceedings Series - 11 
KW  - Computerlinguistik
KW  - Natürliche Sprache
KW  - Subjektivität
KW  - Maschinelles Lernen
KW  - Text Mining
KW  - Sentimentanalyse
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:mh39-85032
UN  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:mh39-85032
SN  - 1736-6305
SS  - 1736-6305
SP  - 254
EP  - 261
PB  - Northern European Association for Language Technology
CY  - Uppsala
ER  -