Mostly-unsupervised statistical segmentation of Japanese Kanji sequences

  • Authors:

  • Rie Kubota Ando;Lillian Lee

  • Affiliations:

  • IBM Thomas J. Watson Research Center, P.O. Box 704, Yorktown Heights, NY 10598, USA e-mail: rie1@us.ibm.com;Department of Computer Science, Cornell University, Ithaca, NY 14853-7501 USA e-mail: llee@cs.cornell.edu

  • Venue:
  • Natural Language Engineering
  • Year:
  • 2003

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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.