Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Combined syntactic and semantic Kernels for text classification
ECIR'07 Proceedings of the 29th European conference on IR research
Syntactic and semantic structure for opinion expression detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Rank learning for factoid question answering with linguistic and semantic constraints
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Reranking models in fine-grained opinion analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Extracting opinion expressions and their polarities: exploration of pipelines and joint models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Using syntactic and semantic structural kernels for classifying definition questions in Jeopardy!
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Structured lexical similarity via convolution kernels on dependency trees
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Semantic separator learning and its applications in unsupervised Chinese text parsing
Frontiers of Computer Science: Selected Publications from Chinese Universities
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In recent years, natural language processing techniques have been used more and more in IR. Among other syntactic and semantic parsing are effective methods for the design of complex applications like for example question answering and sentiment analysis. Unfortunately, extracting feature representations suitable for machine learning algorithms from linguistic structures is typically difficult. In this paper, we describe one of the most advanced piece of technology for automatic engineering of syntactic and semantic patterns. This method merges together convolution dependency tree kernels with lexical similarities. It can efficiently and effectively measure the similarity between dependency structures, whose lexical nodes are in part or completely different. Its use in powerful algorithm such as Support Vector Machines (SVMs) allows for fast design of accurate automatic systems. We report some experiments on question classification, which show an unprecedented result, e.g. 41% of error reduction of the former state-of-the-art, along with the analysis of the nice properties of the approach.