Split-Based Algorithm for Weighted Context-Free Grammar Induction

被引：0

作者：

Gabor, Mateusz ^{[1
]}

Wieczorek, Wojciech ^{[2
]}

Unold, Olgierd ^{[3
]}

机构：

[1] Wroclaw Univ Sci & Technol, Dept Field Theory Elect Circuits & Optoelect, PL-50370 Wroclaw, Poland

[2] Univ Bielsko Biala, Dept Comp Sci & Automat, PL-43309 Bielsko Biala, Poland

[3] Wroclaw Univ Sci & Technol, Dept Comp Engn, PL-50370 Wroclaw, Poland

来源：

APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 03期

关键词：

grammar inference; weighted context-free grammar; split algorithm; unsupervised learning;

D O I：

10.3390/app11031030

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the weights of rules are estimated using a novel Inside-Outside Contrastive Estimation algorithm. The results showed that our approach outperforms in terms of F1 scores of other state-of-the-art methods.

引用

页码：1 / 13

页数：13

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