A Comparison of Data-Driven Automatic Syllabification Methods

被引：0
|
作者：
Adsett, Connie R. [1 ]
Marchand, Yannick [1 ]
机构：
[1] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 1W5, Canada
来源：
STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS | 2009年 / 5721卷
关键词：
Natural language processing; machine learning; automatic syllabification;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Although automatic syllabification is an important component in several natural language tasks, little has been done to compare the results of data-driven methods on a wide range of languages. This article compares the results of five data-driven syllabification algorithms (Hidden Markov Support Vector Machines, IB1, Liang's algorithm, the Look Up Procedure, and Syllabification by Analogy) on nine European languages in order to determine which algorithm performs best over all. Findings show that all algorithms achieve a mean word accuracy across all lexicons of over 90%. However, Syllabification by Analogy performs better than the other algorithms tested with a mean word accuracy of 96.84% (standard deviation of 2.93) whereas Liang's algorithm, the standard for hyphenation (used in TEX), produces the second best results with a mean of 95.67% (standard deviation of 5.70).
引用
收藏
页码：174 / 181
页数：8
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