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Quantifier Selection for Linguistic Data Summarization

Authors: Ingo Glöckner;

Quantifier Selection for Linguistic Data Summarization

Abstract

Fuzzy quantifiers like "about sixty percent" are useful tools for expressing linguistic summaries. But, how can we determine the quantifier which best describes the given data? The quality indicators proposed for quantifier selection still make a rather heuristic impression. The paper therefore investigates a more principled way of controlling quantifier selection: a quantifier should be selected for summarization only when it is used in its prototypical sense. We capture this pragmatic issue of appropriate use by defining an associated pragma quantifier which expresses the paradigmatic cases best described by the considered quantifier. The quantifier selection will be based on an appropriateness score of the summary given by the degree of truth of the pragma quantifier. We further show that pragma quantifiers are typically neither absolute nor proportional, and thus demand generalized models of fuzzy quantification and new implementation techniques.

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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!
3
Average
Average
Average
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