The system for semantic evaluation VENSES (Venice Se- mantic Evaluation System) is organized as a pipeline of two subsystems: the first is a reduced version of GETARUN, our system for Text Un- derstanding. The output of the system is a flat list of augmented head- dependent structures with Grammatical Relations and Semantic Roles labels. The evaluation system is made up of two main modules: the first is a sequence of linguistic rules; the second is a quantitatively based measurement of input structures and predicates. VENSES measures se- mantic similarity which may range from identical linguistic items, to synonymous, lexically similar, or just morphologically derivable. Both modules go through General Consistency checks which are targeted to high level semantic attributes like presence of modality, negation, and opacity operators, temporal and spatial location checks. Results in cws, recall and precision are homogeneous for both training and test corpus and fare higher than 60%.

VENSES – a Linguistically-Based System for Semantic Evaluation

DELMONTE, Rodolfo;TONELLI, Sara
2006

Abstract

The system for semantic evaluation VENSES (Venice Se- mantic Evaluation System) is organized as a pipeline of two subsystems: the first is a reduced version of GETARUN, our system for Text Un- derstanding. The output of the system is a flat list of augmented head- dependent structures with Grammatical Relations and Semantic Roles labels. The evaluation system is made up of two main modules: the first is a sequence of linguistic rules; the second is a quantitatively based measurement of input structures and predicates. VENSES measures se- mantic similarity which may range from identical linguistic items, to synonymous, lexically similar, or just morphologically derivable. Both modules go through General Consistency checks which are targeted to high level semantic attributes like presence of modality, negation, and opacity operators, temporal and spatial location checks. Results in cws, recall and precision are homogeneous for both training and test corpus and fare higher than 60%.
Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/39458
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