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• W1998974009 abstract "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." @default.
• W1998974009 created "2016-06-24" @default.
• W1998974009 creator A5065472000 @default.
• W1998974009 creator A5076876084 @default.
• W1998974009 date "2003-06-01" @default.
• W1998974009 modified "2023-09-27" @default.
• W1998974009 title "Mostly-unsupervised statistical segmentation of Japanese kanji sequences" @default.
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• W1998974009 doi "https://doi.org/10.1017/s1351324902002954" @default.
• W1998974009 hasPublicationYear "2003" @default.
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