This paper proposes a fast and simple unsupervised word segmentation algorithm that utilizes the local predictability of adjacent character sequences, while searching for a leaste...
This paper describes a new approach to the analysis of Poisson point processes, in time (1D) or space (2D), which is based on the minimum description length (MDL) framework. Speci...
In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our...
We propose a simple two-level hierarchical probability model for unsupervised word segmentation. By treating words as strings composed of morphemes/phonemes which are themselves c...