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Part of book or chapter of book
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https://doi.org/10.1007/114245...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
Conference object . 2017
Data sources: DBLP
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Gradience, Constructions and Constraint Systems

Authors: Philippe Blache; Jean-Philippe Prost;

Gradience, Constructions and Constraint Systems

Abstract

This paper is concerned with the question of quantifying gradient degrees of acceptability by introducing the notion of Density in the context of constructional constraint language processing. We first present here our framework for language processing, where all linguistic knowledge is represented by means of constraints. The grammar itself is a constraint system. A constraint is a relation among categories, which encodes a linguistic property. But in contrast to more traditional constraint-based approaches, a constraint can hold and be assessed independently from the structure. In this context, we then introduce the notion of density, based on proportions of satisfied and violated linguistic properties. Our intuition is that density can be used as a means to measure fuzzy notions such as syntactic complexity or as a criterion to identify gradient levels of acceptability. We present and discuss early experimental results concerning density.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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