Segmentation and UR Acquisition with UR Constraints
- Max Nelson (University of Massachusetts Amherst)
Abstract
This paper presents a model that treats segmentation and underlying representation acquisition as parallel, interacting processes. A probability distribution over mappings from underlying to surface forms is defined us- ing a Maximum Entropy grammar which weights a set of underlying representation constraints (URCs) (Apoussidou, 2007; Pater et al., 2012). URCs are induced from observed surface strings and used to generate candidates. Structural ambiguity arising from the com- parison of segmented outputs to unsegmented surface strings is handled with Expectation Maximization (Dempster et al., 1977; Jarosz, 2013). The model successfully learns a simple voicing assimilation rule and segmentation via correspondences between surface phones and input meanings. The trained grammar is also able to segment novel forms affixed with familiar morphemes.
Keywords: segmentation, underlying representation, phonological learning, maximum entropy, expectation maximization
How to Cite:
Nelson, M., (2019) “Segmentation and UR Acquisition with UR Constraints”, Society for Computation in Linguistics 2(1), 60-68. doi: https://doi.org/10.7275/zc9d-pn56
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