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.
FBK-IRST: Kernel Methods for Semantic Relation Extraction
Giuliano, Claudio;Lavelli, Alberto;Pighin, Daniele;Romano, Lorenza
2007-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
