Discovering characteristic patterns from collections of classical Japanese poems

Mayumi Yamasaki, Masayuki Takeda, Tomoko Fukuda, Ichirō Nanri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Waka is a form of traditional Japanese poetry with a 1300- year history. In this paper, we attempt to discover characteristics common to a collection of waka poems. As a formalism for characteristics, we use regular patterns where the constant parts are limited to sequences of auxiliary verbs and postpositional particles. We call such patterns fushi. The problem is to find automatically significant fushi patterns that characterize the poems. Solving this problem requires a reliable significance measure for the patterns. Brāzma et al. (1996) proposed such a measure according to the MDL principle. Using this method, we report successful results in finding patterns from five anthologies. Some of the results are quite stimulating, and we hope that they will lead to new discoveries. Based on our experience, we also propose a pattern-based text data mining system. Further research into waka poetry is now proceeding using this system.

Original languageEnglish
Title of host publicationDiscovery Science - 1st International Conference, DS 1998, Proceedings
EditorsSetsuo Arikawa, Hiroshi Motoda
PublisherSpringer Verlag
Pages129-141
Number of pages13
ISBN (Print)3540653902, 9783540653905
DOIs
Publication statusPublished - 1998
Event1st International Conference on Discovery Science, DS 1998 - Fukuoka, Japan
Duration: Dec 14 1998Dec 16 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1532
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Discovery Science, DS 1998
Country/TerritoryJapan
CityFukuoka
Period12/14/9812/16/98

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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