A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Relation extraction and the influence of automatic named-entity recognition
ACM Transactions on Speech and Language Processing (TSLP)
Discovery and evaluation of non-taxonomic relations in domain ontologies
International Journal of Metadata, Semantics and Ontologies
Extracting position relations from the web
Proceedings of the eleventh international workshop on Web information and data management
Refining non-taxonomic relation labels with external structured data to support ontology learning
Data & Knowledge Engineering
A knowledge-rich approach to identifying semantic relations between nominals
Information Processing and Management: an International Journal
FBK-IRST: Semantic relation extraction using Cyc
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Using local alignments for relation recognition
Journal of Artificial Intelligence Research
Supporting natural language processing with background knowledge: coreference resolution case
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Automatically structuring domain knowledge from text: An overview of current research
Information Processing and Management: an International Journal
A structural approach to extracting Chinese position relations from web pages
Journal of Web Engineering
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We present an approach for semantic relation extraction between nominals that combines shallow and deep syntactic processing and semantic information using kernel methods. Two information sources are considered: (i) the whole sentence where the relation appears, and (ii) WordNet synsets and hypernymy relations of the candidate nominals. Each source of information is represented by kernel functions. In particular, five basic kernel functions are linearly combined and weighted under different conditions. The experiments were carried out using support vector machines as classifier. The system achieves an overall F1 of 71.8% on the Classification of Semantic Relations between Nominals task at SemEval-2007.