<p> Connectionist models of language acquisition typically have difficulty with systematicity, or the ability for the network to generalize its limited experience with language to novel utterances. In this way, connectionist systems learning grammar from a set of example sentences tend to store a set of specific instances, rather than a generalized abstract knowledge of the process of grammatical combination. Further, recent models that do show limited systematicity do so at the expense of simultaneously storing explicit lexical knowledge, and also make use of both developmentally-implausible training data and biologically-implausible learning rules. Consequently, this research program develops a novel unsupervised neural network architecture, and applies this architecture to the problem of systematicity in language models.</p> <p> In the first of several studies, a connectionist architecture capable of simultaneously storing explicit and separate representations of both conceptual and grammatical information is developed, where this architecture is a hybrid of both a self-organizing map and an intra-layer Hebbian associative network. Over the course of several studies, this architecture's capacity to acquire linguistic grammar is evaluated, where the architecture is progressively refined until it is capable of acquiring a benchmark grammar consisting of several difficult clausal sentence structures - though it must acquire this grammar at the level of grammatical category, rather than the lexical level.</p> <p> The final study bridges the gap between the lexical and grammatical category levels, and
develops an activation function based on a semantic feature co-occurrence metric. In concert
with developmentally-plausible sensorimotor grounded conceptual representations, it is shown
that a network using this metric is able to undertake a process of semantic bootstrapping, and
successfully acquire separate explicit representations at the level of the concept, part-of-speech category, and grammatical sequence. This network demonstrates broadly systematic behaviour on a difficult test of systematicity, and extends its knowledge of grammar to novel sensorimotor-grounded words.</p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/19051 |
| Date | 10 1900 |
| Creators | Jansen, Peter |
| Contributors | Watter, Scott, Psychology |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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