144,539 research outputs found
Cognitive modelling of language acquisition with complex networks
ABSTRACT Cognitive modelling is a well-established computational intelligence tool, which is very useful for studying cognitive phenomena, such as young children's first language acquisition. Specifically, linguistic modelling has recently benefited greatly from complex network theory by modelling large sets of empirical linguistic data as complex networks, thereby illuminating interesting new patterns and trends. In this chapter, we show how simple network analysis techniques can be applied to the study of language acquisition, and we argue that they reveal otherwise hidden information. We also note that a key network parameter -the ranked frequency distribution of the links -provides useful knowledge about the data, even though it had been previously neglected in this domain
Pronoun Processing and Interpretation by L2 Learners of Italian: Perspectives from Cognitive Modelling
How do second language learners acquire form-meaning associations in the second language that are inconsistent with their first language? In this study, we focus on subject pronouns in Italian and Dutch. A native speaker of the non-null subject language Dutch learning the null subject language Italian as a second language will not only have to learn to use and comprehend null pronouns, but will also have to learn to use and comprehend overt pronouns differently in the L2 than in the L1. The interpretation of Italian overt pronouns, but not of Dutch overt pronouns or Italian null pronouns, has been argued to require perspective taking, specifically the use of hypotheses about the conversational partner’s communicative choices to guide one’s own choices. Therefore, a related question is how perspective taking and cognitive constraints influence L2 acquisition of such forms. Using computational cognitive modelling, this study explores two learning scenarios. In cognitive model 1, second language acquisition proceeds in the same way as first language acquisition and is based on the same grammar. In cognitive model 2, second language acquisition differs from first language acquisition and involves the construction of a partly different grammar. Our results suggest that the second scenario may be cognitively more plausible than the first one. Furthermore, our models explain why second language learners of Italian perform less native-like on overt pronouns than on null pronouns
Input and Intake in Language Acquisition
This dissertation presents an approach for a productive way forward in the study of language acquisition, sealing the rift between claims of an innate linguistic hypothesis space and powerful domain general statistical inference. This approach breaks language acquisition into its component parts, distinguishing the input in the environment from the intake encoded by the learner, and looking at how a statistical inference mechanism, coupled with a well defined linguistic hypothesis space could lead a learn to infer the native grammar of their native language. This work draws on experimental work, corpus analyses and computational models of Tsez, Norwegian and English children acquiring word meanings, word classes and syntax to highlight the need for an appropriate encoding of the linguistic input in order to solve any given problem in language acquisition
A plea for more interactions between psycholinguistics and natural language processing research
A new development in psycholinguistics is the use of regression analyses on tens of thousands of words, known as the megastudy approach. This development has led to the collection of processing times and subjective ratings (of age of acquisition, concreteness, valence, and arousal) for most of the existing words in English and Dutch. In addition, a crowdsourcing study in the Dutch language has resulted in information about how well 52,000 lemmas are known. This information is likely to be of interest to NLP researchers and computational linguists. At the same time, large-scale measures of word characteristics developed in the latter traditions are likely to be pivotal in bringing the megastudy approach to the next level
Age of acquisition predicts rate of lexical evolution
The processes taking place during language acquisition are proposed to influence language evolution. However, evidence demonstrating the link between language learning and language evolution is, at best, indirect, constituting studies of laboratory-based artificial language learning studies or computational simulations of diachronic change. In the current study, a direct link between acquisition and evolution is established, showing that for two hundred fundamental vocabulary items, the age at which words are acquired is a predictor of the rate at which they have changed in studies of language evolution. Early-acquired words are more salient and easier to process than late-acquired words, and these early-acquired words are also more stably represented within the community's language. Analysing the properties of these early-acquired words potentially provides insight into the origins of communication, highlighting features of words that have been ultra-conserved in language. (C) 2014 Elsevier B.V. All rights reserved
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Modeling German Word Order Acquisition via Bayesian Inference
Perfors et al. (2011) introduced a Bayesian model selection inference system as a child language acquisition model, and demonstrated in the case of English child language input that without any prior bias, such a system would prefer a probabilistic context-free grammar (PCFG) that used hierarchical structure over a regular grammar representing linear phrase structure. However, this system by Perfors et al. (2011) is limited as a computational model because it can only compare PCFGs that all parse the same data, which is likely to include errors, especially in the case of large data sets. In fact, in the German child language corpus that we consider, transcription and part-of-speech (POS) tagging result in so many errors that the corpus no longer appears representative of a child\u27s input. This illustrates that the assumption that such corpora are always appropriate for computational child language acquisition modeling is misleading. Here we propose a method of comparing syntactic hypotheses compatible with different subsets of a child-directed speech corpus by identifying and countering the implications of different data subset sizes on the likelihood component of the Perfors-type Bayesian model selection scheme. We apply this approach in a case study of word-order acquisition in German
Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation
We evaluated a simple computational model of productive vocabulary acquisition, applied to simulating two case studies of 7-year-old children with developmental word-finding difficulties across four core behavioural tasks. Developmental models were created, which captured the deficits of each child. In order to predict the effects of intervention, we exposed the computational models to simulated behavioural interventions of two types, targeting the improvement of either phonological or semantic knowledge. The model was then evaluated by testing the predictions from the simulations against the actual results from an intervention study carried out with the two children. For one child it was predicted that the phonological intervention would be effective, and the semantic intervention would not. This was borne out in the behavioural study. For the second child, the predictions were less clear and depended on the nature of simulated damage to the model. The behavioural study found an effect of semantic but not phonological intervention. Through an explicit computational simulation, we therefore employed intervention data to evaluate our theoretical understanding of the processes underlying acquisition of lexical items for production and how they may vary in children with developmental language difficulties
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Minimally supervised induction of morphology through bitexts
textA knowledge of morphology can be useful for many natural language processing systems. Thus, much effort has been expended in developing accurate computational tools for morphology that lemmatize, segment and generate new forms. The most powerful and accurate of these have been manually encoded, such endeavors being without exception expensive and time-consuming. There have been consequently many attempts to reduce this cost in the development of morphological systems through the development of unsupervised or minimally supervised algorithms and learning methods for acquisition of morphology. These efforts have yet to produce a tool that approaches the performance of manually encoded systems.
Here, I present a strategy for dealing with morphological clustering and segmentation in a minimally supervised manner but one that will be more linguistically informed than previous unsupervised approaches. That is, this study will attempt to induce clusters of words from an unannotated text that are inflectional variants of each other. Then a set of inflectional suffixes by part-of-speech will be induced from these clusters. This level of detail is made possible by a method known as alignment and transfer (AT), among other names, an approach that uses aligned bitexts to transfer linguistic resources developed for one language–the source language–to another language–the target. This approach has a further advantage in that it allows a reduction in the amount of training data without a significant degradation in performance making it useful in applications targeted at data collected from endangered languages. In the current study, however, I use English as the source and German as the target for ease of evaluation and for certain typlogical properties of German. The two main tasks, that of clustering and segmentation, are approached as sequential tasks with the clustering informing the segmentation to allow for greater accuracy in morphological analysis.
While the performance of these methods does not exceed the current roster of unsupervised or minimally supervised approaches to morphology acquisition, it attempts to integrate more learning methods than previous studies. Furthermore, it attempts to learn inflectional morphology as opposed to derivational morphology, which is a crucial distinction in linguistics.Linguistic
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Directionality and complexity of L1 transfer in L2 acquisition: Evidence from L2 Chinese discourse
Abstract
First language (L1) transfer is a common phenomenon in second language (L2) acquisition. However, it will be argued in this article that although there are indeed pervasive influences of learners’ L1 in L2 acquisition, L1 transfer is not everywhere and it can be directional. We compare data from Chang’s (2001b. Discourse effects on the second language acquisition of English and Chinese dative structures. Honolulu: University of Hawai’i at Manoa PhD dissertation, 2004. Discourse effects on EFL learners’ production of dative constructions. Journal of Kaohsiung University of Applied Sciences 33. 145–169.) studies of Chinese-speaking learners of English with data of our study of English-speaking learners of Chinese to examine whether their L2 English discourse and L2 Chinese discourse are equally influenced by their L1 discourse rules. We focus on learners’ answers to wh-questions with a double object construction or a prepositional object construction. The results demonstrate that L1 transfer takes place in Chinese-speaking learners’ L2 English discourse but not in English-speaking learners’ L2 Chinese discourse. This directionality of L1 transfer is accounted for on the basis of computational complexity of linguistic structures involved and on an economical consideration.</jats:p
Prosodic cues enhance infants’ sensitivity to nonadjacent regularities
In language, grammatical dependencies often hold between items that are not immediately adjacent to each other. Acquiring these nonadjacent dependencies is crucial for learning grammar. However, there are poten-tially infinitely many dependencies in the language input. How does the infant brain solve this computational learning problem? Here, we demonstrate that while rudimentary sensitivity to nonadjacent regularities may be present relatively early, robust and reliable learning can only be achieved when convergent statistical and per-ceptual, specifically prosodic cues, are both present, helping the infant brain detect the building blocks that form a nonadjacent dependency. This study contributes to our understanding of the neural foundations of rule learning that pave the way for language acquisition
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