In this paper, we propose innovative repre- sentations for automatic classification of verbs according to mainstream linguistic theories, namely VerbNet and FrameNet. First, syntac- tic and semantic structures capturing essential lexical and syntactic properties of verbs are defined. Then, we design advanced similarity functions between such structures, i.e., seman- tic tree kernel functions, for exploiting distri- butional and grammatical information in Sup- port Vector Machines. The extensive empir- ical analysis on VerbNet class and frame de- tection shows that our models capture mean- ingful syntactic/semantic structures, which al- lows for improving the state-of-the-art.

Verb Classification using Distributional Similarity in Syntactic and Semantic Structures

Moschitti, Alessandro;
2012

Abstract

In this paper, we propose innovative repre- sentations for automatic classification of verbs according to mainstream linguistic theories, namely VerbNet and FrameNet. First, syntac- tic and semantic structures capturing essential lexical and syntactic properties of verbs are defined. Then, we design advanced similarity functions between such structures, i.e., seman- tic tree kernel functions, for exploiting distri- butional and grammatical information in Sup- port Vector Machines. The extensive empir- ical analysis on VerbNet class and frame de- tection shows that our models capture mean- ingful syntactic/semantic structures, which al- lows for improving the state-of-the-art.
Association for Computer Linguistics
Jeju Island, Korea
The Association for Computer Linguistics
978-1-937284-24-4
D., Croce; Moschitti, Alessandro; R., Basili; M., Palmer
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/95273
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