Fuzzy Generalised Quantifiers for Natural Language in Categorical Compositional Distributional Semantics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00355212" target="_blank" >RIV/68407700:21230/21:00355212 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-53654-1_6" target="_blank" >http://dx.doi.org/10.1007/978-3-030-53654-1_6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-53654-1_6" target="_blank" >10.1007/978-3-030-53654-1_6</a>

Result language
angličtina
Original language name
Fuzzy Generalised Quantifiers for Natural Language in Categorical Compositional Distributional Semantics
Original language description
Recent work on compositional distributional models shows that bialgebras over finite dimensional vector spaces can be applied to treat generalised quantifiers for natural language. That technique requires one to construct the vector space over powersets, and therefore is computationally costly. In this paper, we overcome this problem by considering fuzzy versions of quantifiers along the lines of Zadeh, within the category of many valued relations. We show that this category is a concrete instantiation of the compositional distributional model. We show that the semantics obtained in this model is equivalent to the semantics of the fuzzy quantifiers of Zadeh. As a result, we are now able to treat fuzzy quantification without requiring a powerset construction.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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