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Strong Learning of Probabilistic Tree Adjoining Grammars
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Consistent unsupervised estimators for anchored PCFGs
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Consistent Unsupervised Estimators for Anchored PCFGs
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In: Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03410673 ; Transactions of the Association for Computational Linguistics, 2020, 8, ⟨10.1162/tacl_a_00323⟩ (2020)
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Consistent Unsupervised Estimators for Anchored PCFGs
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 409-422 (2020) (2020)
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Changing the lexicon of ‘Cardiac Rehabilitation’: a progressive step
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Introduction to the special topic on grammar induction, representation of language and language learning
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Using Contextual Representations to Efficiently Learn Context-Free Languages
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In: ISSN: 1532-4435 ; EISSN: 1533-7928 ; Journal of Machine Learning Research ; https://hal.archives-ouvertes.fr/hal-00607098 ; Journal of Machine Learning Research, Microtome Publishing, 2010, 11, pp.2707-2744 (2010)
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Abstract:
International audience ; We present a polynomial update time algorithm for the inductive inference of a large class of context-free languages using the paradigm of positive data and a membership oracle. We achieve this result by moving to a novel representation, called Contextual Binary Feature Grammars (CBFGs), which are capable of representing richly structured context-free languages as well as some context sensitive languages. These representations explicitly model the lattice structure of the distribution of a set of substrings and can be inferred using a generalisation of distributional learning. This formalism is an attempt to bridge the gap between simple learnable classes and the sorts of highly expressive representations necessary for linguistic representation: it allows the learnability of a large class of context-free languages, that includes all regular languages and those context-free languages that satisfy two simple constraints. The formalism and the algorithm seem well suited to natural language and in particular to the modeling of first language acquisition. Preliminary experimental results confirm the effectiveness of this approach.
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Keyword:
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; context-free language; grammatical inference; membership queries; positive data only
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URL: https://hal.archives-ouvertes.fr/hal-00607098/document https://hal.archives-ouvertes.fr/hal-00607098 https://hal.archives-ouvertes.fr/hal-00607098/file/Learning_CBFG.pdf
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