Split-Based Algorithm for Weighted Context-Free Grammar Induction

Autor: Wojciech Wieczorek, Mateusz Gabor, Olgierd Unold
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer science
media_common.quotation_subject
02 engineering and technology
unsupervised learning
lcsh:Technology
lcsh:Chemistry
Set (abstract data type)
0202 electrical engineering
electronic engineering
information engineering

Weighted context-free grammar
General Materials Science
lcsh:QH301-705.5
Instrumentation
media_common
Fluid Flow and Transfer Processes
Grammar
lcsh:T
Process Chemistry and Technology
General Engineering
Grammar inference
021001 nanoscience & nanotechnology
split algorithm
lcsh:QC1-999
Grammar induction
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Unsupervised learning
020201 artificial intelligence & image processing
lcsh:Engineering (General). Civil engineering (General)
0210 nano-technology
grammar inference
Algorithm
lcsh:Physics
weighted context-free grammar
Zdroj: Applied Sciences
Volume 11
Issue 3
Applied Sciences, Vol 11, Iss 1030, p 1030 (2021)
ISSN: 2076-3417
DOI: 10.3390/app11031030
Popis: 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.
Databáze: OpenAIRE