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Relevant bibliographies by topics / Regular Grammar Induction

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Academic literature on the topic 'Regular Grammar Induction'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "Regular Grammar Induction"

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Schaefer, Steven, Nathan Varner, Pedro Henrique Azevedo de Amorim, and Max S. New. "Intrinsic Verification of Parsers and Formal Grammar Theory in Dependent Lambek Calculus." Proceedings of the ACM on Programming Languages 9, PLDI (2025): 773–96. https://doi.org/10.1145/3729281.

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We present Dependent Lambek Calculus (Lambek D ), a domain-specific dependent type theory for verified parsing and formal grammar theory. In Lambek D , linear types are used as a syntax for formal grammars, and parsers can be written as linear terms. The linear typing restriction provides a form of intrinsic verification that a parser yields only valid parse trees for the input string. We demonstrate the expressivity of this system by showing that the combination of inductive linear types and dependency on non-linear data can be used to encode commonly used grammar formalisms such as regular a

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Liu, Linlin. "English Pedagogical Grammar: Teaching Present Perfect and Present Perfect Continuous by Deductive and Inductive Approaches." Studies in English Language Teaching 8, no. 3 (2020): p138. http://dx.doi.org/10.22158/selt.v8n3p138.

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This research endeavor aims to present the English Pedagogical Grammar Teaching, discussing the use and form of the present perfect and present perfect continuous tenses, and the regular verbs’ past participle and irregular verbs’ past participle. The study is based on two main assumptions that cause difficulties for learners of English, namely, the forms of verbs and the difficulty of distinguishing between the present from the past simple tenses. The study discusses the use of deductive and inductive approaches in English pedagogical grammar teaching, and evaluates these approaches from A-fa

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Ahuja, Kabir, Vidhisha Balachandran, Madhur Panwar, et al. "Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers." Transactions of the Association for Computational Linguistics 13 (February 12, 2024): 121–41. https://doi.org/10.1162/tacl_a_00733.

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Abstract Transformers trained on natural language data have been shown to exhibit hierarchical generalization without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could cause such preference for hierarchical generalization. We extensively experiment with transformers trained on five synthetic, controlled datasets using several training objectives and show that, while objectives such as sequence-to-sequence modeling, classification, etc., often fail to lead to hierarchical generalization, the langua

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Nagy, Shaan, Jinwoo Kim, Thomas Reps, and Loris D’Antoni. "Automating Unrealizability Logic: Hoare-Style Proof Synthesis for Infinite Sets of Programs." Proceedings of the ACM on Programming Languages 8, OOPSLA2 (2024): 113–39. http://dx.doi.org/10.1145/3689715.

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Automated verification of all members of a (potentially infinite) set of programs has the potential to be useful in program synthesis, as well as in verification of dynamically loaded code, concurrent code, and language properties. Existing techniques for verification of sets of programs are limited in scope and unable to create or use interpretable or reusable information about sets of programs. The consequence is that one cannot learn anything from one verification problem that can be used in another. Unrealizability logic (UL), proposed by Kim et al. as the first Hoare-style proof system to

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-, Dr S. Ansar Hussain, and Dr R. V. Jayanth Kasyap -. "Tools of Language Learning - A Pedagogical Perspective." International Journal For Multidisciplinary Research 6, no. 1 (2024). http://dx.doi.org/10.36948/ijfmr.2024.v06i01.11595.

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In the present technological breakthroughs English language learning has turned out to be more receptive, accessible, exciting and challenging task at the same time in terms of implementation. This advancement has made the assigned task more interesting, interactive and thrilled. Though electronic devices cannot substitute a resourceful teacher, these would certainly help to improve receptive skill and at the same time a remedial teaching measures for average learners. One such active source is YouTube videos teaching English grammar to higher secondary students. Lessons on core grammar topics

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Dissertations / Theses on the topic "Regular Grammar Induction"

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Grand, Maxence. "Apprentissage de Modèle d'Actions basé sur l'Induction Grammaticale Régulière pour la Planification en Intelligence Artificielle." Electronic Thesis or Diss., Université Grenoble Alpes, 2022. http://www.theses.fr/2022GRALM044.

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Le domaine de l’intelligence artificielle vise à concevoir des agents autonomes capables de percevoir, d’apprendre et d’agir sans aucune intervention humaine pour accomplir des tâches complexes. Pour accomplir des tâches complexes, l’agent autonome doit planifier les meilleures actions possibles et les exécuter. Pour ce faire, l’agent autonome a besoin d’un modèle d’action. Un modèle d’action est une représentation sémantique des actions qu’il peut exécuter. Dans un modèle d’actions, une action est représentée à l’aide (1) d’une précondition: l’ensemble des conditions qui doivent être satisfai

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Packer, Thomas L. "Scalable Detection and Extraction of Data in Lists in OCRed Text for Ontology Population Using Semi-Supervised and Unsupervised Active Wrapper Induction." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4258.

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Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known a

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Gebhardt, Kilian. "Induction, Training, and Parsing Strategies beyond Context-free Grammars." 2019. https://tud.qucosa.de/id/qucosa%3A71398.

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This thesis considers the problem of assigning a sentence its syntactic structure, which may be discontinuous. It proposes a class of models based on probabilistic grammars that are obtained by the automatic refinement of a given grammar. Different strategies for parsing with a refined grammar are developed. The induction, refinement, and application of two types of grammars (linear context-free rewriting systems and hybrid grammars) are evaluated empirically on two German and one Dutch corpus.

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Book chapters on the topic "Regular Grammar Induction"

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Unold, Olgierd. "Regular Language Induction with Grammar-based Classifier System." In Engineering the Computer Science and IT. InTech, 2009. http://dx.doi.org/10.5772/7768.

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Umili, Elena, and Roberto Capobianco. "DeepDFA: Automata Learning through Neural Probabilistic Relaxations." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240596.

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In this work, we introduce DeepDFA, a novel approach to identifying Deterministic Finite Automata (DFAs) from traces, harnessing a differentiable yet discrete model. Inspired by both the probabilistic relaxation of DFAs and Recurrent Neural Networks (RNNs), our model offers interpretability post-training, alongside reduced complexity and enhanced training efficiency compared to traditional RNNs. Moreover, by leveraging gradient-based optimization, our method surpasses combinatorial approaches in both scalability and noise resilience. Validation experiments conducted on target regular languages

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Conference papers on the topic "Regular Grammar Induction"

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Belcak, Peter, David Hofer, and Roger Wattenhofer. "A Neural Model for Regular Grammar Induction." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00064.

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