Pregroup Grammars, Their Syntax and Semantics

Sadrzadeh, Mehrnoosh

doi:10.1007/978-3-030-66545-6_10

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Clitic movement in Farsi. Following [Sad07], we model Farsi grammar using the π-augmented pregroup (F, →) which is generated by the atoms n, p, o, s, q, which represent the types of nouns, personal pronouns, direct objects, sentences, and questions, respectively. We also add o and p to represent the clitic forms of direct objects and personal pronouns, and we impose the equivalences: p ⇔ p, o ⇔ o.…”

Section: Chiara Omentioning

confidence: 99%

$π$-augmented pregroups and applications to linguistics

Boboc¹

2023

Preprint

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Section: Chiara Omentioning

confidence: 99%

$π$-augmented pregroups and applications to linguistics

Boboc¹

2023

Preprint

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“…A few suggestions have been put forth to define the constitution of the vector space S. Truth style semantics (Coecke et al, 2010) (Sadrzadeh, 2021) provide easy interpretation of such spaces. There, S is spanned by the vectors − → 1 , representing True, and − → 0 representing False, and a scaled value of these bases represents the truth value of the sentence.…”

Section: Process-state Duality (Informally)mentioning

confidence: 99%

A mathematical neural process model of language comprehension, from syllable to sentence

Kaushik,

Martin

2022

Preprint

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Human beings effortlessly perceive structural meaning from a biophysical signal such as speech or sign. An explanation of the processes underlying this phenomenon needs to account for both the properties of the signal, as well as those of the neural architecture deploying linguistic knowledge. This article approaches the question from a mathematical perspective, providing a neurophysiologically grounded explanation of the process underlying linguistic structure building in the brain. For defining the properties of the signal, we rely on the mathematical linguistics of DisCoCat (Coecke et al. 2010), a syntactically sensitive formalism of distributional meaning that makes no claims about the neural processing underlying sentence comprehension. The neuroscientific architecture derives from a cue-integration model of language comprehension (Martin 2020). In it, the brain infers the latent structure of a cue or signal based on knowledge of the language, through a process of neural coordinate transform. In this work, we demonstrate how the DisCoCat formalism can interface with the neurophysiological process model and describe how the resulting incremental process model can return a formal description at each timestep. Second, we present an extension to show how structure building from phonological segments to syllables can be modelled within a categorial grammar setup, integrating it with our process model. Third, we introduce a temporal metric interpretation on the transformations occuring within the extended DisCoCat formalism for each level of representation - also known as categorical enrichment. As a result of this specification, we obtain a mechanistic account of neural oscillatory readouts during language comprehension.

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“…Pregroups are utilized to encode the grammar of languages, providing an algebraic analysis of sentence parsing [43]. Models for the semantics of positive and negative transitive sentences and relative pronouns are presented in Refs.…”

Section: Quantum Circuits Of Sentences In Two Different Languages And...mentioning

confidence: 99%

“…According to [43], the example sentence "Sara ketab ra az ketabforoushi mikharad" has the following type of assignment: Sara ketab ra az ketabforoushi mikharad.…”

Section: Conversion Of Sentences To Quantum Circuitsmentioning

confidence: 99%

Application of Quantum Natural Language Processing for Language Translation

et al. 2021

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In this paper, we develop compositional vector-based semantics of positive transitive sentences using quantum natural language processing (Q-NLP) to compare the parametrized quantum circuits of two synonymous simple sentences in English and Persian. We propose a protocol based on quantum long short-term memory (Q-LSTM) for Q-NLP to perform various tasks in general but specifically for translating a sentence from English to Persian. Then, we generalize our method to use quantum circuits of sentences as an input for the Q-LSTM cell. This enables us to translate sentences in different languages.Our work paves the way toward representing quantum neural machine translation, which may demonstrate quadratic speedup and converge faster or reaches a better accuracy over classical methods.

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Pregroup Grammars, Their Syntax and Semantics

Cited by 3 publications

References 31 publications

$π$-augmented pregroups and applications to linguistics

$π$-augmented pregroups and applications to linguistics

A mathematical neural process model of language comprehension, from syllable to sentence

Application of Quantum Natural Language Processing for Language Translation

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