Bayesian inference as a cross-linguistic word segmentation strategy: Always learning useful things

摘要

Statistical learning has been proposed as one of the earliest strategies infants could use to segment words out of their native language because it does not rely on language-specific cues that must be derived from existing knowledge of the words in the language. Statistical word segmentation strategies using Bayesian inference have been shown to be quite successful for English (Goldwater et al. 2009), even when cognitively inspired processing constraints are integrated into the inference process (Pearl et al. 2011, Phillips & Pearl 2012). Here we test this kind of strategy on child-directed speech from seven languages to evaluate its effectiveness cross-linguistically, with the idea that a viable strategy should succeed in each case. We demonstrate that Bayesian inference is indeed a viable cross-linguistic strategy, provided the goal is to identify useful units of the language, which can range from sub-word morphology to whole words to meaningful word combinations.