Recession segmentation: simpler online word segmentation using limited resources

Authors:
Constantine Lignos;Charles Yang
Affiliations:
University of Pennsylvania;University of Pennsylvania
Venue:
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Year:
2010

Citing 6
Cited 1

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Abstract

In this paper we present a cognitively plausible approach to word segmentation that segments in an online fashion using only local information and a lexicon of previously segmented words. Unlike popular statistical optimization techniques, the learner uses structural information of the input syllables rather than distributional cues to segment words. We develop a memory model for the learner that like a child learner does not recall previously hypothesized words perfectly. The learner attains an F-score of 86.69% in ideal conditions and 85.05% when word recall is unreliable and stress in the input is reduced. These results demonstrate the power that a simple learner can have when paired with appropriate structural constraints on its hypotheses.