Challenge Problems for a Theory of Degree Multiplication (with partial answer key)

This paper offers a theory of degree multiplication in natural language semantics. Motivation for the development such a theory comes from proportional readings of quantity words and rate expressions such as miles per hour. After laying out a set of ‘challenge problems’ that any good theory of degree multiplication should be able to handle, I set about solving them, borrowing mathematical tools from quantity calculus. These algebraic foundations are integrated into a compositional Montagovian framework, yielding a system that can solve, or partially solve, some of the problems.

Is Aristotle the Forefather of Informal Logic?

I argue that Aristotle takes a ‘natural language semantics’ approach to logic, which is consistent with the general attitudes one finds in informal logic today. Although his position is complex, Aristotle emphasizes the intensional rather than the extensional side of argument evaluation. He does not take a truth-functional approach to semantics, but an approach that elucidates the illative mechanism through an understanding of natures. This comes close to what informal logicians insist on. The informal logic movement was, to a very large extent, a Canadian initiative, prominently featuring authors such as Johnson, Blair, Govier, and many others.

exture Oriented Scene Generation from Natural Language Text Description using 3D Coloured Objects

To understand language, we need an understanding of the world around us. Language describes the world and provides symbols with which we represent meaning. Still, much knowledge about the world is so obvious that it is rarely explicitly stated. It is uncommon for people to state that chairs are usually on the floor and upright, and that you usually eat a cake from a plate on a table. Knowledge of such common facts provides the context within which people communicate with language. Therefore, to create practical systems that can interact with the world and communicate with people, we need to leverage such knowledge to interpret language in context. Scene generation can be used to achieve an ability to generate 3D scenes on basis of text description. A model capable of learning natural language semantics or interesting pattern to generate abstract idea behind scene composition is interesting [1].Scene generation from text involves several fields like NLP, artificial intelligence, computer vision and machine learning. This paper focuses on optimally arranging objects in a room with focus on the orientation of the objects with respect to the floor, wall and ceiling of a room along with textures. Our model suggest a novel framework which can be used as a tool to generate scene where anyone without 3D Modeling.

Generative Linguistics Meets Normative Inferentialism

This is the second installment of a two-part essay. Limitations of space prevented the publication of the full essay in a previous issue of the Journal (Pereplyotchik 2020). My overall goal is to outline a strategy for integrating generative linguistics with a broadly pragmatist approach to meaning and communication. Two immensely useful guides in this venture are Robert Brandom and Paul Pietroski. Squarely in the Chomskyan tradition, Pietroski’s recent book, Conjoining Meanings, offers an approach to natural-language semantics that rejects foundational assumptions widely held amongst philosophers and linguists. In particular, he argues against extensionalism—the view that meanings are (or determine) truth and satisfaction conditions. Having arrived at the same conclusion by way of Brandom’s deflationist account of truth and reference, I’ll argue that both theorists have important contributions to make to a broader anti-extensionalist approach to language. Part 1 of the essay was largely exegetical, laying out what I see as the core aspects of Brandom’s normative inferentialism (1) and Pietroski’s naturalistic semantics (2). Now, in Part 2, I argue that there are many convergences between these two theoretical frameworks and, contrary to first appearances, very few points of substantive disagreement between them. If the integration strategy that I propose is correct, then what appear to be sharply contrasting commitments are better seen as interrelated verbal differences that come down to different—but complementary—explanatory goals. The residual disputes are, however, stubborn. I end by discussing how to square Pietroski’s commitment to predicativism with Brandom’s argument that a predicativist language is in principle incapable of expressing ordinary conditionals.

Conditionals, Paradox, and Probability

This is a volume of essays in philosophy and linguistics in tribute to Dorothy Edgington, the first woman to hold a chair in philosophy in the University of Oxford. The volume focuses on topics to which Edgington has made many important contributions including conditionals, vagueness, the paradox of knowability, and probability. The volume will be of interest to philosophers, linguists, and psychologists with an interest in philosophical logic, natural language semantics, and reasoning.

Perception, Types and Frames

We present a view of perception as the classification of objects and events in terms of types in the sense of TTR, a Type Theory with Records. We argue that such types can be used to give a formal model of concepts and cognitive processing involving concepts. This yields a view that natural language semantics is based on our cognitive perceptual ability. The paper provides an overview of some key ideas in TTR including the important notion of record type. We suggest that record types can be used to model frames in a way that relates to the Düsseldorf notion of frame as well as those of Fillmore and Barsalou.

A Qualitative Similarity Framework for the Interpretation of Natural Language Similarity Expressions

In this paper, a representational framework is presented featuring a qualitative notion of similarity. It is aimed at issues of natural language semantics, in particular the semantics of expressions of similarity and sameness and their role in comparison and ad-hoc kind formation. The framework makes use of attribute spaces, which are well-established in AI and also in some branches of natural language semantics, e.g., frame-based approaches (Barsalou 1992). What distinguishes attribute spaces and representations as proposed in this paper is the idea of systems of predicates on attribute spaces corresponding to predicates on the domain. On the worldy side, a domain includes a set of relevant predicates talking about individuals. These predicates have counterparts on the representational side talking about points of an attribute space. Counterpart predicates are required to be consistent with their originals; more precisely, they have to agree in truth-value on the set of positive and negative exemplars thereby approximating the original predicates. Moreover, counterpart predicates will be assumed to have convex and open extensions. This system facilitates a qualitative notion of similarity which is suited to account for the meaning of natural language similarity expressions and, furthermore, their role in comparison and ad-hoc kind formation.

Category theory

This chapter aims to introduce sufficient category theory to enable a formal understanding of the rest of the book. It first introduces the fundamental notion of a category. It then introduces functors, which are maps between categories. Next it introduces natural transformations, which are natural ways of mapping between functors. The stage is then set to at last introduces monads, which are defined in terms of functors and natural transformations. The last part of the chapter provides a compositional calculus with monads for natural language semantics (in other words, a logic for working with monads) and then relates the compositional calculus to Glue Semantics and to a very simple categorial grammar for parsing. The chapter ends with some exercises to aid understanding.