Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic labeling of semantic roles
Computational Linguistics
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Automatic learning of textual entailments with cross-pair similarities
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning noun-modifier semantic relations with corpus-based and WordNet-based features
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
A knowledge-rich approach to identifying semantic relations between nominals
Information Processing and Management: an International Journal
Using local alignments for relation recognition
Journal of Artificial Intelligence Research
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This system uses a background knowledge base to identify semantic relations between base noun phrases in English text, as evaluated in SemEval 2007, Task 4. Training data for each relation is converted to statements in the Scone Knowledge Representation Language. At testing time a new Scone statement is created for the sentence under scrutiny, and presence or absence of a relation is calculated by comparing the total semantic distance between the new statement and all positive examples to the total distance between the new statement and all negative examples.