scholarly journals Formal Syntax as a Phylogenetic Method

2020 ◽  
pp. 145-182
Author(s):  
Cristina Guardiano ◽  
Giuseppe Longobardi ◽  
Guido Cordoni ◽  
Paola Crisma
Keyword(s):  
Formal Syntax ◽  
Phylogenetic Method

AN INTENSIONAL LEIBNIZ SEMANTICS FOR ARISTOTELIAN LOGIC

2010 ◽  
Vol 3 (2) ◽  
pp. 262-272 ◽  
Author(s):  
KLAUS GLASHOFF
Keyword(s):  
Natural Deduction ◽  
Predicate Logic ◽  
Formal System ◽  
Aristotelian Logic ◽  
Categorical Logic ◽  
Characteristic Numbers ◽  
Formal Syntax ◽  
Philosophical Standpoint ◽  
Classical Philosophy

Since Frege’s predicate logical transcription of Aristotelian categorical logic, the standard semantics of Aristotelian logic considers terms as standing for sets of individuals. From a philosophical standpoint, this extensional model poses problems: There exist serious doubts that Aristotle’s terms were meant to refer always to sets, that is, entities composed of individuals. Classical philosophy up to Leibniz and Kant had a different view on this question—they looked at terms as standing for concepts (“Begriffe”). In 1972, Corcoran presented a formal system for Aristotelian logic containing a calculus of natural deduction, while, with respect to semantics, he still made use of an extensional interpretation. In this paper we deal with a simple intensional semantics for Corcoran’s syntax—intensional in the sense that no individuals are needed for the construction of a complete Tarski model of Aristotelian syntax. Instead, we view concepts as containing or excluding other, “higher” concepts—corresponding to the idea which Leibniz used in the construction of his characteristic numbers. Thus, this paper is an addendum to Corcoran’s work, furnishing his formal syntax with an adequate semantics which is free from presuppositions which have entered into modern interpretations of Aristotle’s theory via predicate logic.

scholarly journals On Learning Interpreted Languages with Recurrent Models

2022 ◽  
pp. 1-13
Author(s):  
Denis Paperno
Keyword(s):  
Natural Language ◽  
Data Processing ◽  
Syntactic Structure ◽  
Neural Nets ◽  
Training Data ◽  
Sequential Data ◽  
Extensive Training ◽  
Formal Syntax ◽  
Compositional Interpretation

Abstract Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.

scholarly journals Questions in Bantu languages: prosodies and positions

2011 ◽  
Vol 55 ◽  
pp. 182
Author(s):  
Laura J. Downing
Keyword(s):  
Doctoral Student ◽  
Project Leader ◽  
Research Teams ◽  
Bantu Languages ◽  
Bantu Language ◽  
Formal Syntax ◽  
Wh Questions ◽  
Cooperative Project

The papers in this volume were originally presented at the Workshop on Bantu Wh-questions, held at the Institut des Sciences de l’Homme, Université Lyon 2, on 25-26 March 2011, which was organized by the French-German cooperative project on the Phonology/Syntax Interface in Bantu Languages (BANTU PSYN). This project, which is funded by the ANR and the DFG, comprises three research teams, based in Berlin, Paris and Lyon. The Berlin team, at the ZAS, is: Laura Downing (project leader) and Kristina Riedel (post-doc). The Paris team, at the Laboratoire de phonétique et phonologie (LPP; UMR 7018), is: Annie Rialland (project leader), Cédric Patin (Maître de Conférences, STL, Université Lille 3), Jean-Marc Beltzung (post-doc), Martial Embanga Aborobongui (doctoral student), Fatima Hamlaoui (post-doc). The Lyon team, at the Dynamique du Langage (UMR 5596) is: Gérard Philippson (project leader) and Sophie Manus (Maître de Conférences, Université Lyon 2). These three research teams bring together the range of theoretical expertise necessary to investigate the phonology-syntax interface: intonation (Patin, Rialland), tonal phonology (Aborobongui, Downing, Manus, Patin, Philippson, Rialland), phonology-syntax interface (Downing, Patin) and formal syntax (Riedel, Hamlaoui). They also bring together a range of Bantu language expertise: Western Bantu (Aboronbongui, Rialland), Eastern Bantu (Manus, Patin, Philippson, Riedel), and Southern Bantu (Downing).  

ARTIFICIAL INTELLIGENCE IN DATABASE SECURITY

2004 ◽  
Vol 18 (01) ◽  
pp. 3-17
Author(s):  
YUN BAI ◽  
YAN ZHANG
Keyword(s):  
Artificial Intelligence ◽  
Object Oriented ◽  
Database Systems ◽  
Database System ◽  
Database Security ◽  
Security Policies ◽  
Formal Approach ◽  
Formal Syntax ◽  
Data Objects ◽  
Object Oriented Database

In this paper, we propose a formal approach of Artificial Intelligence (AI) in securing object oriented database systems. We combine the specification of object oriented database with security policies and provide its formal syntax and semantics. The properties in the inheritance of authorizations in object oriented database system and reasoning about authorizations on data objects are also investigated in detail.

Neo-Gricean Pragmatics

2016 ◽  
Author(s):  
Yan Huang
Keyword(s):  
State Of The Art ◽  
Linguistic Theory ◽  
Logical Operators ◽  
What Is Said ◽  
Formal Syntax ◽  
Gricean Pragmatics ◽  
Pragmatic Intrusion

The aim of this chapter is to provide a state-of-the-art survey of classical and especially neo-Gricean pragmatics, focusing on the bipartite model put forward by Horn and the trinitarian model advanced by Levinson. It assesses the role neo-Gricean pragmatics plays in effecting a radical simplification of the lexicon, semantics, and formal syntax in linguistic theory respectively, covering lexical narrowing, lexical cloning, lexical blocking, and lexicalization asymmetry in logical operators, and concentrating on pragmatic intrusion into what is said, Grice’s circle, and the pragmatics–semantics interface, and anaphora and binding.

Reconstructing Phylogenetic Relationships Based on Repeat Sequence Similarities

2019 ◽  
Author(s):  
Daniel Vitales ◽  
Sònia Garcia ◽  
Steven Dodsworth
Keyword(s):  
Phylogenetic Relationships ◽  
Sequence Similarity ◽  
Information Source ◽  
Repeat Sequence ◽  
Combined Application ◽  
Novel Approach ◽  
Genome Wide ◽  
History Of ◽  
Sequence Similarities ◽  
Phylogenetic Method

AbstractA recent phylogenetic method based on genome-wide abundance of different repeat types proved to be useful in reconstructing the evolutionary history of several plant and animal groups. Here, we demonstrate that an alternative information source from the repeatome can also be employed to infer phylogenetic relationships among taxa. Specifically, this novel approach makes use of the repeat sequence similarity matrices obtained from the comparative clustering analyses of RepeatExplorer 2, which are subsequently transformed to between-taxa distance matrices. These pairwise matrices are used to construct neighbour-joining trees for each of the top most-abundant clusters and they are finally summarized in a consensus network. This methodology was tested on three groups of angiosperms and one group of insects, resulting in congruent evolutionary hypotheses compared to more standard systematic analyses based on commonly used DNA markers. We propose that the combined application of these phylogenetic approaches based on repeat abundances and repeat sequence similarities could be helpful to understand mechanisms governing genome and repeatome evolution.

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