Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Journal of Intelligent Information Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support Vector Machines Based on a Semantic Kernel for Text Categorization
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Kernel methods for relation extraction
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A semantic kernel to exploit linguistic knowledge
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Word sense disambiguation for exploiting hierarchical thesauri in text classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Kernel-based relation extraction from investigative data
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Using Graph-Kernels to Represent Semantic Information in Text Classification
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Complex question answering: unsupervised learning approaches and experiments
Journal of Artificial Intelligence Research
Reverse engineering of tree kernel feature spaces
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Re-ranking models based-on small training data for spoken language understanding
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Syntactic/semantic structures for textual entailment recognition
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Syntactic and semantic structure for opinion expression detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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
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
Learning discriminative projections for text similarity measures
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Semantic convolution kernels over dependency trees: smoothed partial tree kernel
Proceedings of the 20th ACM international conference on Information and knowledge management
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
Distributional models and lexical semantics in convolution kernels
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Ontology-guided feature engineering for clinical text classification
Journal of Biomedical Informatics
Verb classification using distributional similarity in syntactic and semantic structures
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Language independent semantic kernels for short-text classification
Expert Systems with Applications: An International Journal
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The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset.