We have shown that a model that expects function words on the left periphery performs more accurate word segmentation on English, where function words do indeed typically occur on the left periphery, leaving open the question: how could a learner determine whether function words generally appear on the left or the right periphery of phrases in the language they are learning?
in order to do this it is imperative to develop better methods than the problematic "Harmonic Mean" estimator used here for calculating the evidence (i.e., the marginal probability of the data) that can handle the combination of discrete and continuous hidden structure that occur in computational linguistic models.