An empirical study of automated dictionary construction for information extraction in three domains
Artificial Intelligence - Special volume on empirical methods
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploring syntactic features for relation extraction using a convolution tree kernel
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Tree kernels for semantic role labeling
Computational Linguistics
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Hybrid learning of dependency structures from heterogeneous linguistic resources
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Joint extraction of entities and relations for opinion recognition
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Opinion Question Answering: Towards a Unified Approach
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Syntactic and semantic structure for opinion expression detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Using sequence kernels to identify opinion entities in Urdu
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Generalization methods for in-domain and cross-domain opinion holder extraction
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Challenges and solutions in the opinion summarization of user-generated content
Journal of Intelligent Information Systems
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
An enhanced semantic tree kernel for sentiment polarity classification
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Factored semantic sequence kernel for sentiment polarity classification
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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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.