In this paper, we introduce a new distri- butional method for modeling predicate- argument thematic fit judgments. We use a syntax-based DSM to build a prototyp- ical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typi- cal role fillers), and then we compute the- matic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the- art system, and achieves better or compa- rable results to those reported in the liter- ature for the other unsupervised systems. Moreover, it provides an explicit represen- tation of the features characterizing verb- specific semantic roles.

Measuring Thematic Fit with Distributional Feature Overlap

Santus, Enrico
Primo
;
Chersoni, Emmanuele
Co-primo
;
Lenci, Alessandro
Co-primo
;
2017-01-01

Abstract

In this paper, we introduce a new distri- butional method for modeling predicate- argument thematic fit judgments. We use a syntax-based DSM to build a prototyp- ical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typi- cal role fillers), and then we compute the- matic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the- art system, and achieves better or compa- rable results to those reported in the liter- ature for the other unsupervised systems. Moreover, it provides an explicit represen- tation of the features characterizing verb- specific semantic roles.
2017
978-1-945626-83-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/891685
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