Towards a Unified Model of Language Acquisition

In this theoretical paper, we first review and rebut standard criticisms against distributional approaches to language acquisition. We then present two closely-related models that use distributional analysis. The first deals with the acquisition of vocabulary, the second with grammatical development. We show how these two models can be combined with a semantic network grown using Hebbian learning, and briefly illustrate the advantages of this combination. An important feature of this hybrid system is that it combines two different types of distributional learning, the first based on order, and the second based on co-occurrences within a context

BEYOND STATISTICAL LEARNING IN THE ACQUISITION OF PHRASE STRUCTURE

The notion that children use statistical distributions present in the input to acquire various aspects of linguistic knowledge has received considerable recent attention. But the roles of learner's initial state have been largely ignored in those studies. What remains unclear is the nature of learner's contribution. At least two possibilities exist. One is that all that learners do is to collect and compile accurately predictive statistics from the data, and they do not have antecedently specified set of possible structures (Elman, et al. 1996; Tomasello 2000). On this view, outcome of the learning is solely based on the observed input distributions. A second possibility is that learners use statistics to identify particular abstract syntactic representations (Miller & Chomsky 1963; Pinker 1984; Yang 2006). On this view, children have predetermined linguistic knowledge on possible structures and the acquired representations have deductive consequences beyond what can be derived from the observed statistical distributions alone. This dissertation examines how the environment interacts with the structure of the learner, and proposes a linking between distributional approach and nativist approach to language acquisition. To investigate this more general question, we focus on how infants, adults and neural networks acquire the phrase structure of their target language. This dissertation presents seven experiments, which show that adults and infants can project their generalizations to novel structures, while the Simple Recurrent Network fails. Moreover, it will be shown that learners' generalizations go beyond the stimuli, but those generalizations are constrained in the same ways that natural languages are constrained. This is compatible with the view that statistical learning interacts with inherent representational system, but incompatible with the view that statistical learning is the sole mechanism by which the existence of phrase structure is discovered. This provides novel evidence that statistical learning interacts with innate constraints on possible representations, and that learners have a deductive power that goes beyond the input data. This suggests that statistical learning is used merely as a method for mapping the surface string to abstract representation, while innate knowledge specifies range of possible grammars and structures

Dialect Variation, Optionality, and the Learnability Guarantee

In acqumng a language the child is often faced with developing a grammar on the basis of input from a range of adults who speak different dialects or idiolects and whose grammars are not therefore identical. The fact that language acquisition is not subject to failure in such circumstances must mean that input from any combination of possible language varieties is guaranteed to trigger the development of a language system. The implications of this for the nature of Universal Grammar and the language acquisition process are explore

Choosing and learning: Semiosis means choice

We examine the possibility of shifting the concept of choice to the centre of the semiotic theory of learning. Thus, we define sign process (meaning-making) through the concept of choice: semiosis is the process of making choices between simultaneously provided options. We define semiotic learning as leaving traces by choices, while these traces influence further choices. We term such traces of choices memory. Further modification of these traces (constraints) will be called habituation. Organic needs are homeostatic mechanisms coupled with choice-making. Needs and habits result in motivatedness. Semiosis as choice-making can be seen as a complementary description of the Peircean triadic model of semiosis; however, this can fit also the models of meaning-making worked out in other shools of semiotics. We also provide a sketch for a joint typology of semiosis and learning

Enhancing the expressiveness of linguistic structures

In the information society large amounts of information are being generated and transmitted constantly, especially in the most natural way for humans, i.e., natural language. Social networks, blogs, forums, and Q&A sites are a dynamic Large Knowledge Repository. So, Web 2.0 contains structured data but still the largest amount of information is expressed in natural language. Linguistic structures for text recognition enable the extraction of structured information from texts. However, the expressiveness of the current structures is limited as they have been designed with a strict order in their phrases, limiting their applicability to other languages and making them more sensible to grammatical errors. To overcome these limitations, in this paper we present a linguistic structure named ?linguistic schema?, with a richer expressiveness that introduces less implicit constraints over annotations

A Primer

UID/ELT/04097/2019Translation agents, in their professional lives, face a reality characterised by uncertainty. This uncertainty is rooted in the complexity of translation systems. Systems consisting of translators, clients, texts, languages (among other elements) are complex systems, as they are composed of several interacting elements, in which what happens to one element influences the reactions of other elements, which in turn influence the original element, in a cascade of interactions which can only be analysed as emergent phenomena. For those who work in the field of translation, the behaviour of these emergent phenomena seems to be the result of pure chance. Each agent is in a particular position at the intersection of several of these systems, exposed to complexity and unpredictability in a particular and, in itself, unpredictable (and complex) way. We must find a way to deal with uncertainty and complexity immediately, while researchers in the field of complexity in translation continue to seek a more complete description of emergent phenomena. In this article, I present five tactics which are a foundation of an antifragile strategy to deal with uncertainty: avoiding fragility, optionality and redundancy, trial and error, via negativa, barbell. This five-fold strategy is based on the twin concepts of fragility and antifragility, as described by Nassim Nicholas Taleb (2012).publishersversionpublishe