Unsupervised Statistical Segmentation of Japanese Kanji Strings
Year: 1999
Type: article
Abstract: Word segmentation is an important issue in Japanese language processing because Japanese is written without space delimiters between words. We propose a simple dictionary-less method to segment Japanese kanji sequences into words based solely on character $n$-gram counts from an unannotated corpus. The performance was often better than that of rule-based morphological analyzers over a variety of both standard and novel error metrics.
Authors: Rie Kubota Ando, Lillian Lee
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: GE Foundation
OpenAlex ID: W76754543