34.1952, Books: Dependency as Modality, Parsing as Permutation: Kogkalidis

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LINGUIST List: Vol-34-1952. Mon Jun 19 2023. ISSN: 1069 - 4875.

Subject: 34.1952, Books: Dependency as Modality, Parsing as Permutation: Kogkalidis

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Date: 13-Jun-2023
From: Tessa Arneri [lotdissertations-fgw at uva.nl]
Subject: Dependency as Modality, Parsing as Permutation: Kogkalidis


Title: Dependency as Modality, Parsing as Permutation
Subtitle: A Neurosymbolic Perspective on Categorial Grammars
Series Title: LOT Dissertation Series
Publication Year: 2023
Publisher: Netherlands Graduate School of Linguistics / Landelijke
(LOT)
                http://www.lotpublications.nl/
Book URL: https://www.lotpublications.nl/dependency-as-modality-parsin
g-as-permutation

Author: Konstantinos Kogkalidis
Paperback: ISBN: 9789460934339 Pages: 239 Price: Europe EURO 36
Abstract:

This thesis presents a novel approach to the processing and
representation of natural language syntax and semantics, combining
symbolic and neural techniques.

The symbolic core is powered by a linear type system that uses
modalities to capture dependency structures on top of
function-argument relations, enabling a more flexible and expressive
way of representing grammatical utterances.
The practical applications of this approach are showcased through the
computational study of Dutch, utilizing a set of tools and resources
developed specifically for this purpose. These include a large
proofbank, i.e., a collection of sentences associated with
tectogrammatic theorems and their corresponding programs, supported by
an extensive type lexicon, which provides type assignments to almost
one million lexical tokens within a given linguistic context. Parsing
is handled by a combination of static type-checking, a
state-of-the-art supertagger based on heterogeneous graph
convolutions, and a massively parallel proof search component
formulated as a neural bijection learner.

Overall, this thesis demonstrates the power of an integrated
neurosymbolic approach to natural language processing combining the
best of both worlds - the symbolic representation of meaning and the
statistical power of modern neural networks.

Linguistic Field(s): Computational Linguistics

Written In: English (eng)

See this book announcement on our website:
http://old.linguistlist.org/pubs/books/get-book.cfm?BookID=171714



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