http://hdl.handle.net/20.500.12128/13207
Tytuł: | Unsupervised Statistical Learning of Context-free Grammar |
Autor: | Unold, Olgierd Gabor, Mateusz Wieczorek, Wojciech |
Słowa kluczowe: | Formal Languages; Grammar Inference |
Data wydania: | 2020 |
Wydawca: | Setúbal : SciTePress |
Źródło: | Ana Rocha, Luc Steels, Jaap van den Herik (red.), ICAART 2020: Proceedings of the 12th International Conference on Agents and Artificial : Natural Language Processing in Artificial Intelligence, Vol. 1, (S. 431-438). Setúbal : SciTePress |
Abstrakt: | In this paper, we address the problem of inducing (weighted) context-free grammar (WCFG) on data given. The induction is performed by using a new model of grammatical inference, i.e., weighted Grammar-based Classifier System (wGCS). wGCS derives from learning classifier systems and searches grammar structure using a genetic algorithm and covering. Weights of rules are estimated by using a novelty Inside-Outside Contrastive Estimation algorithm. The proposed method employs direct negative evidence and learns WCFG both form positive and negative samples. Results of experiments on three synthetic context-free languages show that wGCS is competitive with other statistical-based method for unsupervised CFG learning. |
URI: | http://hdl.handle.net/20.500.12128/13207 |
DOI: | 10.5220/0009383604310438 |
ISBN: | 978-989-758-395-7 |
Pojawia się w kolekcji: | Książki/rozdziały (WNŚiT) |
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Unold_Unsupervised_Statistical_Learning_of.pdf | 339,59 kB | Adobe PDF | Przejrzyj / Otwórz |
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