Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences
<|reference_start|>Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences: 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.<|reference_end|>
@article{ando2002mostly-unsupervised,
    title={Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences},
    author={Rie Kubota Ando and Lillian Lee},
    journal={Natural Language Engineering 9 (2), pp. 127--149, 2003},
    year={2002},
    doi={10.1017/S1351324902002954},
    archivePrefix={arXiv},
    eprint={cs/0205009},
    primaryClass={cs.CL}
}