West Flemish verb-based discourse markers and the articulation of the Speech Act layer

This paper focuses on the West Flemish discourse markers located at the edge of the clause. After a brief survey of the distribution of discourse markers in WF, the paper proposes a syntactic analysis of the discourse markers ne and we. Based on the distribution of these discourse markers, of vocatives and of dislocated DPs, an articulated speech act layer is elaborated which corroborates the proposals in Hill (). It is postulated that there is a syntactic relation between particles used as discourse markers and vocatives. The paper offers further support for the grammaticalization of pragmatic features at the interface between syntax and discourse and for the hypothesis that the relevant computation at the interface is of the same nature as that in Narrow Syntax

The cognitive cost of deriving implicature: A reaction-time study

If A asks B “Do you like berries?”, and B replies “I like some berries,” B would infer that A does not like all kinds of berries. Such inference derived by negating the stronger alternative (all) is known as the scalar implicature (SI). Earlier experimental studies showed that computation of SI requires additional processing time compared to literal interpretation, and hence they argued that derivation of implicature is cost-demanding. Some recent experiments, however, found that derivation of implicature does not require any additional processing cost. The present study re-examines the comprehension of implicature using a Truth Value Judgement task. The hypothesis of this study is that the computation of implicature is as immediate as the computation of literal meaning if the sentences are preceded by prior context and communicative intent as in real conversation. The study uses a two-between-subject design where 32 native English speakers were required to read a ‘context’, followed by a ‘question’ and an ‘answer’. The context followed by the question either demanded the lower-bounded ‘literal’ meaning or the upper-bounded ‘pragmatic’ meaning of the under-informative answers which is the implicature. The result indicates that when a prior context and a clear communicative intent guide the hearer toward the intended meaning, both literal and pragmatic meaning comprehension is immediate. The result certainly indicates against the Default Inference accounts, but it also opposes the Literal-first hypothesis of the Contextualist school. The result strongly supports the Constraint-Based account of implicature derivation and brings additional support to the studies which argue for immediate implicature computation

Pragmatic Holism

The reductionist/holist debate seems an impoverished one, with many participants appearing to adopt a position first and constructing rationalisations second. Here I propose an intermediate position of pragmatic holism, that irrespective of whether all natural systems are theoretically reducible, for many systems it is completely impractical to attempt such a reduction, also that regardless if whether irreducible `wholes' exist, it is vain to try and prove this in absolute terms. This position thus illuminates the debate along new pragmatic lines, and refocusses attention on the underlying heuristics of learning about the natural world

Computation in Physical Systems: A Normative Mapping Account

The relationship between abstract formal procedures and the activities of actual physical systems has proved to be surprisingly subtle and controversial, and there are a number of competing accounts of when a physical system can be properly said to implement a mathematical formalism and hence perform a computation. I defend an account wherein computational descriptions of physical systems are high-level normative interpretations motivated by our pragmatic concerns. Furthermore, the criteria of utility and success vary according to our diverse purposes and pragmatic goals. Hence there is no independent or uniform fact to the matter, and I advance the ‘anti-realist’ conclusion that computational descriptions of physical systems are not founded upon deep ontological distinctions, but rather upon interest-relative human conventions. Hence physical computation is a ‘conventional’ rather than a ‘natural’ kind

The Nature and Function of Content in Computational Models

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature and function of representation in computational cognitive models

Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier

The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation significantly. For the prediction, we train linear and kernelized support vector machine classifiers, providing an out-of-sample performance guarantee if properly regularized, converting to distributionally robust classifiers. For the unit commitment problem, we solve a mixed-integer second-order cone problem. Our results based on the IEEE 6-bus and 118-bus test systems show that the kernelized SVM with proper regularization outperforms other classifiers, reducing the computational time by a factor of 1.7. In addition, if there is a tight computational limit, while the unit commitment problem without warm start is far away from the optimal solution, its warmly started version can be solved to optimality within the time limit

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images. Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.Comment: ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video abstract at https://youtu.be/NHQsDaycwy

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches