The nature of statistical learning theory
The nature of statistical learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd 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
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
Kernels on linguistic structures for answer extraction
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Re-ranking models for spoken language understanding
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Annotating spoken dialogs: from speech segments to dialog acts and frame semantics
SRSL '09 Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language
Effective use of WordNet semantics via kernel-based learning
CONLL '05 Proceedings of the Ninth 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
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Syntactic and semantic structure for opinion expression detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Spoken language understanding via supervised learning and linguistically motivated features
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
Kernel-based reranking for named-entity extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Hypotheses selection criteria in a reranking framework for spoken language understanding
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
The design of practical language applications by means of statistical approaches requires annotated data, which is one of the most critical constraint. This is particularly true for Spoken Dialog Systems since considerably domain-specific conceptual annotation is needed to obtain accurate Language Understanding models. Since data annotation is usually costly, methods to reduce the amount of data are needed. In this paper, we show that better feature representations serve the above purpose and that structure kernels provide the needed improved representation. Given the relatively high computational cost of kernel methods, we apply them to just re-rank the list of hypotheses provided by a fast generative model. Experiments with Support Vector Machines and different kernels on two different dialog corpora show that our re-ranking models can achieve better results than state-of-the-art approaches when small data is available.