Modelling syntactic development in a cross-linguistic context

Mainstream linguistic theory has traditionally assumed that children come into the world with rich innate knowledge about language and grammar. More recently, computational work using distributional algorithms has shown that the information contained in the input is much richer than proposed by the nativist approach. However, neither of these approaches has been developed to the point of providing detailed and quantitative predictions about the developmental data. In this paper, we champion a third approach, in which computational models learn from naturalistic input and produce utterances that can be directly compared with the utterances of language-learning children. We demonstrate the feasibility of this approach by showing how MOSAIC, a simple distributional analyser, simulates the optional-infinitive phenomenon in English, Dutch, and Spanish. The model accounts for young children's tendency to use both correct finites and incorrect (optional) infinitives in finite contexts, for the generality of this phenomenon across languages, and for the sparseness of other types of errors (e.g., word order errors). It thus shows how these phenomena, which have traditionally been taken as evidence for innate knowledge of Universal Grammar, can be explained in terms of a simple distributional analysis of the language to which children are exposed

Semantic Types, Lexical Sorts and Classifiers

We propose a cognitively and linguistically motivated set of sorts for lexical semantics in a compositional setting: the classifiers in languages that do have such pronouns. These sorts are needed to include lexical considerations in a semantical analyser such as Boxer or Grail. Indeed, all proposed lexical extensions of usual Montague semantics to model restriction of selection, felicitous and infelicitous copredication require a rich and refined type system whose base types are the lexical sorts, the basis of the many-sorted logic in which semantical representations of sentences are stated. However, none of those approaches define precisely the actual base types or sorts to be used in the lexicon. In this article, we shall discuss some of the options commonly adopted by researchers in formal lexical semantics, and defend the view that classifiers in the languages which have such pronouns are an appealing solution, both linguistically and cognitively motivated

Generativity and dynamic opacity for abstract types (extended version)

The standard formalism for explaining abstract types is existential quantification. While it provides a sufficient model for type abstraction in entirely statically typed languages, it proves to be too weak for languages enriched with forms of dynamic typing, where parametricity is violated. As an alternative approach to type abstraction that addresses this shortcoming we present a calculus for dynamic type generation. It features an explicit construct for generating new type names and relies on coercions for managing abstraction boundaries between generated types and their designated representation. Sealing is represented as a generalized form of these coercions. The calculus maintains abstractions dynamically without restricting type analysis

The Generative Capacity of Digital Artifacts: A Mapping of the Field

The concept of generativity as the capacity of a technology or a system to be malleable by diverse groups of actors in unanticipated ways has recently gained considerable traction in information systems research. We review a sample of the body of knowledge and identify that scholars commonly investigated generativity in conjunction with digital infrastructures and digital platforms, both of which are complex, networked, and evolving socio-technical systems. Interestingly, other types of digital artifacts have been neglected, despite our initial assumption that the distinct attributes (e.g., reprogrammability, distributedness) of any digital artifact match well with generativity. The literature review also reveals that innovation brought about heterogeneous groups of actors is universally regarded as the goal of generativity, discounting the possibility of exploiting generative systems towards other valuable ends such as organizational agility. Furthermore, scholars commonly discuss generativity in conjunction with the logic of modularity, leading to unresolved questions on how these two concepts might complement each other. Another important contribution of this paper is the systematization of various meanings of generativity, spanning from the philosophical–e.g., generative mechanisms in critical realist research–to a more literal understanding, for instance generativity as synonym to ‘creation of a particular solution’

Types For Modules

The programming language Standard ML is an amalgam of two, largely orthogonal, languages. The Core language expresses details of algorithms and data structures. The Modules language expresses the modular architecture of a software system. Both languages are statically typed, with their static and dynamic semantics specified by a formal definition. Over the past decade, Standard ML Modules has been the source of inspiration for much research into the type-theoretic foundations of modules languages. Despite these efforts, a proper type-theoretic understanding of its static semantics has remained elusive. In this thesis, we use Type Theory as a guideline to reformulate the unconventional static semantics of Modules, providing a basis for useful extensions to the Modules language. Our starting point is a stylised presentation of the existing static semantics of Modules, parameterised by an arbitrary Core language. We claim that the type-theoretic concepts underlying Modules are type parameterisation, type quantification and subtyping. We substantiate this claim by giving a provably equivalent semantics with an alternative, more type-theoretic presentation. In particular, we show that the notion of type generativity corresponds to existential quantification over types. In contrast to previous accounts, our analysis does not involve first-order dependent types. Our first extension generalises Modules to higher-order, allowing modules to take parameterised modules as arguments, and return them as results. We go beyond previous proposals for higher-order Modules by supporting a notion of type generativity. We give a sound and complete algorithm for type-checking higher-order Modules. Our second extension permits modules to be treated as first-class citizens of an ML-like Core language, greatly extending the range of computations on modules. Each extension arises from a natural generalisation of our type-theoretic semantics. This thesis also addresses two pragmatic concerns. First, we propose a simple approach to the separate compilation of Modules, which is adequate in practice but has theoretical limitations. We suggest a modified syntax and semantics that alleviates these limitations. Second, we study the type inference problem posed by uniting our extensions to higher-order and first-class modules with an implicitly-typed, ML-like Core language. We present a hybrid type inference algorithm that integrates the classical algorithm for ML with the type-checking algorithm for Modules

Typed open programming : a higher-order, typed approach to dynamic modularity and distribution

In this dissertation we develop an approach for reconciling open programming the development of programs that support dynamic exchange of higher-order values with other processes with strong static typing in programming languages. We present the design of a concrete programming language, Alice ML, that consists of a conventional functional language extended with a set of orthogonal features like higher-order modules, dynamic type checking, higher-order serialisation, and concurrency. On top of these a flexible system of dynamic components and a simple but expressive notion of distribution is realised. The central concept in this design is the package, a first-class value embedding a module along with its interface type, which is dynamically checked whenever the module is extracted. Furthermore, we develop a formal model for abstract types that is not invalidated by the presence of primitives for dynamic type inspection, as is the case for the standard model based on existential quantification. For that purpose, we present an idealised language in form of an extended -calculus, which can express dynamic generation of types. This calculus is the first to combine and explore the interference of sealing and type inspection with higher-order singleton kinds, a feature for expressing sharing constraints on abstract types. A novel notion of abstracton kinds classifies abstract types. Higher-order type and kind coercions allow for modular translucent encapsulation of values at arbitrary type.In dieser Dissertation entwickeln wir einen programmiersprachlichen Ansatz zur Verbindung offener Programmierung der Entwicklung von Programmen, die das dynamische Laden und Austauschen höherstufiger Werte mit anderen Prozessen erlauben mit starker statischer Typisierung. Wir stellen das Design einer konkreten Programmiersprache namens Alice ML vor. Sie besteht aus einer konventionellen funktionalen Sprache, die um einen Satz orthogonaler Konzepte wie höherstufige Modularisierung, dynamische Typüberprüfung, höherstufige Serialisierung und Nebenläufigkeit erweitert wurde. Darauf aufbauend ist ein flexibles System dynamischer Komponenten sowie ein einfacher aber expressiver Ansatz fur Verteilung verwirklicht. Zentral ist dabei das Konzept eines Pakets (package), welches ein Modul in Kombination mit seinem Schnittstellentyp in einen Wert einbettet, und bei der Extraktion des Moduls eine dynamische Typüberprüfung vornimmt. Weiterhin entwickeln wir einen theoretischen Ansatz zur Modellierung von abstrakten Typen, welcher im Gegensatz zum herkömmlichen formalen Modell existentieller Quantifizierung auch in Gegenwart dynamischer Typinspektion gültig ist. Zu diesem Zweck definieren wir eine idealisierte Sprache in Form eines erweiterten λ-Kalküls, der dynamische Typgenerierung ausdrucken kann. Der Kalkül kombiniert diese erstmals mit höherstufigen Singleton Kinds, einem Sprachkonstrukt, welches Gleichheit von Typen ausdrücken kann. Zur Klassifizierung abstrakter Typen werden Abstraktions-Kinds als verwandtes Konzept entwickelt. Höherstufige Konversionen auf Term- und Typebene erlauben zudem die nachträgliche modulare Enkapsulierung von Werten beliebigen Typs