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Computational Models of Morphological Learning  

Jordan Kodner

A computational learner needs three things: Data to learn from, a class of representations to acquire, and a way to get from one to the other. Language acquisition is a very particular learning setting that can be defined in terms of the input (the child’s early linguistic experience) and the output (a grammar capable of generating a language very similar to the input). The input is infamously impoverished. As it relates to morphology, the vast majority of potential forms are never attested in the input, and those that are attested follow an extremely skewed frequency distribution. Learners nevertheless manage to acquire most details of their native morphologies after only a few years of input. That said, acquisition is not instantaneous nor is it error-free. Children do make mistakes, and they do so in predictable ways which provide insights into their grammars and learning processes. The most elucidating computational model of morphology learning from the perspective of a linguist is one that learns morphology like a child does, that is, on child-like input and along a child-like developmental path. This article focuses on clarifying those aspects of morphology acquisition that should go into such an elucidating a computational model. Section 1 describes the input with a focus on child-directed speech corpora and input sparsity. Section 2 discusses representations with focuses on productivity, developmental paths, and formal learnability. Section 3 surveys the range of learning tasks that guide research in computational linguistics and NLP with special focus on how they relate to the acquisition setting. The conclusion in Section 4 presents a summary of morphology acquisition as a learning problem with Table 4 highlighting the key takeaways of this article.

Article

Neurolinguistic Approaches in Morphology  

Niels O. Schiller

Neurolinguistic approaches to morphology include the main theories of morphological representation and processing in the human mind, such as full-listing, full-parsing, and hybrid dual-route models, and how the experimental evidence that has been acquired to support these theories uses different neurolinguistic paradigms (visual and auditory priming, violation, long-lag priming, picture-word interference, etc.) and methods (electroencephalography [EEG]/event-related brain potential [ERP], functional magnetic resonance imaging [fMRI], neuropsychology, and so forth).

Article

Discriminative Learning and the Lexicon: NDL and LDL  

Yu-Ying Chuang and R. Harald Baayen

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.