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On the Utility of Conjoint and Compositional Frames and Utterance
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Abstract:
This paper reports the results of a series of connectionist simulations aimed at establishing the value of different types of contexts as predictors of the grammatical categories of words. A comparison is made between ‘compositional’ frames (Monaghan & Christiansen, 2004), and non-compositional or ‘conjoint’ frames (Mintz, 2003). Attention is given to the role of utterance boundaries both as a category to be predicted and as a predictor. The role of developmental constraints is investigated by examining the effect of restricting the analysis to utterance-final frames. In line with results reported by Monaghan and Christiansen compositional frames are better predictors than conjoint frames, though the latter provide a small performance improvement when combined with compositional frames. Utterance boundaries are shown to be detrimental to performance when included as an item to be predicted while improving performance when included as a predictor. The utility of utterance boundaries is further supported by the finding that when the analysis is restricted to utterance-final frames (which are likely to be a particularly important source of information early in development) frames including utterance boundaries are far better predictors than lexical frames.
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Keyword:
Christiansen; conjoint frame; connectionism; frame; grammatical category; lexical frame; Mintz; Monaghan; MOSAIC; non-compositional frame; utterance boundary
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URL: http://bura.brunel.ac.uk/handle/2438/2147
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3 |
Simulating the referential properties of Dutch, German and English Root Infinitives in MOSAIC
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9 |
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Linking working memory and long-term memory: A computational model of the learning of new words
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Jones, G; Gobet, F; Pine, J M. - : Blackwell Publishing. The definitive version is available at onlinelibrary.wiley.com, 2007
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11 |
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12 |
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13 |
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16 |
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17 |
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19 |
The role of input size and generativity in simulating language acquisition.
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Modelling children's negation errors using probabilistic learning in MOSAIC.
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