Given the lack of word delimiters in written Japanese, word segmentation is generally
considered a crucial first step in processing Japanese texts. Typical Japanese segmentation
algorithms rely either on a lexicon and syntactic analysis or on pre-segmented data; but these
are labor-intensive, and the lexico-syntactic techniques are vulnerable to the unknown word
problem. In contrast, we introduce a novel, more robust statistical method utilizing unsegmented
training data. Despite its simplicity, the algorithm yields performance on long kanji sequences
comparable to and sometimes surpassing that of state-of-the-art morphological analyzers
over a variety of error metrics. The algorithm also outperforms another mostly-unsupervised
statistical algorithm previously proposed for Chinese. Additionally, we present a two-level
annotation scheme for Japanese to incorporate multiple segmentation granularities, and
introduce two novel evaluation metrics, both based on the notion of a compatible bracket,
that can account for multiple granularities simultaneously.