Picturing words: The semantics of speech balloons

Semantics traditionally focuses on linguistic meaning. In recent years, the Super Linguistics movement has tried to broaden the scope of inquiry in various directions, including an extension of semantics to talk about the meaning of pictures. There are close similarities between the interpretation of language and of pictures. Most fundamentally, pictures, like utterances, can be either true or false of a given state of affairs, and hence both express propositions (Zimmermann, 2016; Greenberg, 2013; Abusch, 2015). Moreover, sequences of pictures, like sequences of utterances, can be used to tell stories. Wordless picture books, comics, and film are cases in point. In this paper I pick up the project of providing a dynamic semantic account of pictorial story-telling, started by Abusch (2012) and continued by Abusch & Rooth (2017); Maier & Bimpikou (2019); Fernando (2020). More specifically, I propose here a semantics of speech and thought bubbles by adding event reference to PicDRT. To get there I first review the projection-based semantics for pictures (section 1), noting the fundamental distinction between symbolic and iconic meaning that makes speech bubbles especially interesting (section 2). I then review the dynamic PicDRT framework for pictorial narratives (section 3), add events (section 4), and propose an account of speech bubbles as quotational event modification (section 5). I end with a brief look at other conventional, symbolic enrichments in comics (section 6)

Classification systems offer a microcosm of issues in conceptual processing: A commentary on Kemmerer (2016)

This is a commentary on Kemmerer (2016), Categories of Object Concepts Across Languages and Brains: The Relevance of Nominal Classification Systems to Cognitive Neuroscience, DOI: 10.1080/23273798.2016.1198819

Symbolic Approximate Reasoning Within Unbalanced Multi-sets: Application to Autism Diagnosis

International audienceIn most daily activities, humans often use imprecise information derived from appreciation instead of exact measurements to make decisions. Multisets allow the representation of imperfect information in a Knowledge-Based System (KBS), in the multivalued logic context. New facts are deduced using approximate reasoning. In the literature, dealing with imperfect information relies on an implicit assumption: the distribution of terms is uniform on a scale ranging from 0 to 1. Nevertheless, in some cases, a sub-domain of this scale may be more informative and may include more terms. In this work, we focus on approximate reasoning within these sets, known as unbalanced sets, in the context of multi-valued logic. We introduce an approach based on the Generalized Modus Ponens (GMP) model using Generalized Symbolic Modifiers (GSM). The proposed model is implemented in a tool for autism diagnosis by means of unbalanced severity degrees of the Childhood Autism Rating Scale (CARS). We obtain satisfying results on the distinction between autistic and not autistic child compared to psychiatrists diagnosis

Perceptual simulation in conceptual tasks

No abstract available

Automatic case acquisition from texts for process-oriented case-based reasoning

This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.Comment: Sous presse, publication pr\'evue en 201

Using Description Logics for Recognising Textual Entailment

The aim of this paper is to show how we can handle the Recognising Textual Entailment (RTE) task by using Description Logics (DLs). To do this, we propose a representation of natural language semantics in DLs inspired by existing representations in first-order logic. But our most significant contribution is the definition of two novel inference tasks: A-Box saturation and subgraph detection which are crucial for our approach to RTE