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Статті в журналах з теми "Deep syntax":
Kong, Leilei, Zhongyuan Han, Yong Han, and Haoliang Qi. "A Deep Paraphrase Identification Model Interacting Semantics with Syntax." Complexity 2020 (October 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/9757032.
Wu, Xianchao, Takuya Matsuzaki, and Jun’ichi Tsujii. "Improve syntax-based translation using deep syntactic structures." Machine Translation 24, no. 2 (June 2010): 141–57. http://dx.doi.org/10.1007/s10590-010-9081-6.
Zhang, Zhining, Liang Wan, Kun Chu, Shusheng Li, Haodong Wei, and Lu Tang. "JACLNet:Application of adaptive code length network in JavaScript malicious code detection." PLOS ONE 17, no. 12 (December 14, 2022): e0277891. http://dx.doi.org/10.1371/journal.pone.0277891.
Ding, Jiaman, Weikang Fu, and Lianyin Jia. "Deep Forest and Pruned Syntax Tree-Based Classification Method for Java Code Vulnerability." Mathematics 11, no. 2 (January 15, 2023): 461. http://dx.doi.org/10.3390/math11020461.
Gupta, Vikram, Haoyue Shi, Kevin Gimpel, and Mrinmaya Sachan. "Deep Clustering of Text Representations for Supervision-Free Probing of Syntax." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10720–28. http://dx.doi.org/10.1609/aaai.v36i10.21317.
Khasanah, Noor. "Transformational Linguistics and the Implication Towards Second Language Learning." Register Journal 3, no. 1 (July 1, 2016): 23. http://dx.doi.org/10.18326/rgt.v3i1.23-36.
Liang, Hongliang, Lu Sun, Meilin Wang, and Yuxing Yang. "Deep Learning With Customized Abstract Syntax Tree for Bug Localization." IEEE Access 7 (2019): 116309–20. http://dx.doi.org/10.1109/access.2019.2936948.
Chlipala, Adam. "Skipping the binder bureaucracy with mixed embeddings in a semantics course (functional pearl)." Proceedings of the ACM on Programming Languages 5, ICFP (August 22, 2021): 1–28. http://dx.doi.org/10.1145/3473599.
Gupta, Rahul, Aditya Kanade, and Shirish Shevade. "Deep Reinforcement Learning for Syntactic Error Repair in Student Programs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 930–37. http://dx.doi.org/10.1609/aaai.v33i01.3301930.
Amini, Afra, Tiago Pimentel, Clara Meister, and Ryan Cotterell. "Naturalistic Causal Probing for Morpho-Syntax." Transactions of the Association for Computational Linguistics 11 (2023): 384–403. http://dx.doi.org/10.1162/tacl_a_00554.
Дисертації з теми "Deep syntax":
Tse, Daniel Gar-shon. "Chinese CCGbank: Deep derivations and dependencies for Chinese CCG parsing." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9439.
Lim, Steven. "Recommending TEE-based Functions Using a Deep Learning Model." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104999.
Master of Science
Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.
Michell, Theodore William Henry. "The psychasthenia of deep space : evaluating the 'reassertion of space in critical social theory'." Thesis, University College London (University of London), 2002. http://discovery.ucl.ac.uk/4325/.
Senko, Jozef. "Hluboký syntaxí řízený překlad." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234933.
Ribeyre, Corentin. "Méthodes d’analyse supervisée pour l’interface syntaxe-sémantique : de la réécriture de graphes à l’analyse par transitions." Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCC119.
Nowadays, the amount of textual data has become so gigantic, that it is not possible to deal with it manually. In fact, it is now necessary to use Natural Language Processing techniques to extract useful information from these data and understand their underlying meaning. In this thesis, we offer resources, models and methods to allow: (i) the automatic annotation of deep syntactic corpora to extract argument structure that links (verbal) predicates to their arguments (ii) the use of these resources with the help of efficient methods. First, we develop a graph rewriting system and a set of manually-designed rewriting rules to automatically annotate deep syntax in French. Thanks to this approach, two corpora were created: the DeepSequoia, a deep syntactic version of the Séquoia corpus and the DeepFTB, a deep syntactic version of the dependency version of the French Treebank. Next, we extend two transition-based parsers and adapt them to be able to deal with graph structures. We also develop a set of rich linguistic features extracted from various syntactic trees. We think they are useful to bring different kind of topological information to accurately predict predicat-argument structures. Used in an arc-factored second-order parsing model, this set of features gives the first state-of-the-art results on French and outperforms the one established on the DM and PAS corpora for English. Finally, we briefly explore a method to automatically induce the transformation between a tree and a graph. This completes our set of coherent resources and models to automatically analyze the syntax-semantics interface on French and English
Mille, Simon. "Deep stochastic sentence generation : resources and strategies." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283136.
La presente tesis aborda el problema de la generación de textos partiendo desde estructuras profundas; se examina especialmente el papel de un esquema de anotación apropiado para la generación estadística de oraciones. La falta de anotación en varios niveles ha impedido hasta ahora el desarrollo de sistemas de generación estadística desde estructuras abstractas. En primer lugar, se detalla la metodología para anotar corpus en varios niveles (representaciones semánticas, sintácticas profundas, sintácticas superficiales, topológicas y morfológicas), y se presenta su proceso de anotación, manual para el español, y automático para el inglés. Posteriormente, se usan los datos anotados para entrenar y evaluar varios generadores de textos que van más allá del estado del arte actual, en particular porque no contienen reglas para transducciones no isomórficas. Por último, se muestra que estos datos se pueden utilizar también para otros objetivos tales como el análisis sintáctico estadístico de estructuras superficiales y profundas.
Colin, Émilie. "Traitement automatique des langues et génération automatique d'exercices de grammaire." Thesis, Université de Lorraine, 2020. http://www.theses.fr/2020LORR0059.
Our perspectives are educational, to create grammar exercises for French. Paraphrasing is an operation of reformulation. Our work tends to attest that sequence-to-sequence models are not simple repeaters but can learn syntax. First, by combining various models, we have shown that the representation of information in multiple forms (using formal data (RDF), coupled with text to extend or reduce it, or only text) allows us to exploit a corpus from different angles, increasing the diversity of outputs, exploiting the syntactic levers put in place. We also addressed a recurrent problem, that of data quality, and obtained paraphrases with a high syntactic adequacy (up to 98% coverage of the demand) and a very good linguistic level. We obtain up to 83.97 points of BLEU-4*, 78.41 more than our baseline average, without syntax leverage. This rate indicates a better control of the outputs, which are varied and of good quality in the absence of syntax leverage. Our idea was to be able to work from raw text : to produce a representation of its meaning. The transition to French text was also an imperative for us. Working from plain text, by automating the procedures, allowed us to create a corpus of more than 450,000 sentence/representation pairs, thanks to which we learned to generate massively correct texts (92% on qualitative validation). Anonymizing everything that is not functional contributed significantly to the quality of the results (68.31 of BLEU, i.e. +3.96 compared to the baseline, which was the generation of text from non-anonymized data). This second work can be applied the integration of a syntax lever guiding the outputs. What was our baseline at time 1 (generate without constraint) would then be combined with a constrained model. By applying an error search, this would allow the constitution of a silver base associating representations to texts. This base could then be multiplied by a reapplication of a generation under constraint, and thus achieve the applied objective of the thesis. The formal representation of information in a language-specific framework is a challenging task. This thesis offers some ideas on how to automate this operation. Moreover, we were only able to process relatively short sentences. The use of more recent neural modelswould likely improve the results. The use of appropriate output strokes would allow for extensive checks. *BLEU : quality of a text (scale from 0 (worst) to 100 (best), Papineni et al. (2002))
Solár, Peter. "Syntaxí řízený překlad založený na hlubokých zásobníkových automatech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236779.
Genčúrová, Ľubica. "Nové verze zásobníkových automatů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403109.
Seraku, Tohru. "Clefts, relatives, and language dynamics : the case of Japanese." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:0448acc3-dee6-4b1b-9020-95fd84895f24.
Книги з теми "Deep syntax":
Rauh, Gisa. Tiefenkasus, thematische Relationen und Thetarollen: Die Entwicklung einer Theorie von semantischen Relationen. Tübingen: G. Narr, 1988.
Huck, Geoffrey J. Ideology and linguistic theory: Noam Chomsky and the deep structure debates. London: Routledge, 1995.
Schubert, K. Metataxis: Contrastive Dep[endency Syntax for Machine Translation. de Gruyter GmbH, Walter, 1987.
Taylor, Ralph B. How Do We Get to Causal Clarity on Physical Environment-Crime Dynamics? Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.2.
Waters, Keith. Postbop Jazz in the 1960s. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190604578.001.0001.
McNaughton, James. Samuel Beckett and the Politics of Aftermath. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822547.001.0001.
Частини книг з теми "Deep syntax":
Correia, José, Jorge Baptista, and Nuno Mamede. "Syntax Deep Explorer." In Lecture Notes in Computer Science, 189–201. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41552-9_19.
Coüasnon, Bertrand, Ashok Popat, and Richard Zanibbi. "Discussion Group Summary: Graphics Syntax in the Deep Learning Age." In Lecture Notes in Computer Science, 158–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02284-6_13.
"Deep and Surface Structure." In An Introduction to Transformational Syntax, 22–32. Routledge, 2016. http://dx.doi.org/10.4324/9781315461496-9.
"Dependency Syntax: Surface Structure and Deep Structure." In Application of Graph Rewriting to Natural Language Processing, 35–70. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119428589.ch2.
"On some deep structural analogies between syntax and phonology." In Morpheme-internal Recursion in Phonology, 57–116. De Gruyter Mouton, 2020. http://dx.doi.org/10.1515/9781501512582-004.
Harris, Randy Allen. "The Beauty of Deep Structure." In The Linguistics Wars, 15–64. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199740338.003.0002.
Hellmuth, Sam. "Functional complementarity is only skin‐deep: Evidence from Egyptian Arabic for the autonomy of syntax and phonology in the expression of focus." In The Sound Patterns of Syntax, 247–70. Oxford University Press, 2010. http://dx.doi.org/10.1093/acprof:oso/9780199556861.003.0012.
Zheng, Robert Z. "Influence of Multimedia and Cognitive Strategies in Deep and Surface Verbal Processing." In Examining Multiple Intelligences and Digital Technologies for Enhanced Learning Opportunities, 162–83. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0249-5.ch009.
Zheng, Robert Z. "Influence of Multimedia and Cognitive Strategies in Deep and Surface Verbal Processing." In Research Anthology on Applied Linguistics and Language Practices, 341–61. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5682-8.ch012.
Rey, Georges. "The Basics of Generative Grammars." In Representation of Language, 45–92. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198855637.003.0002.
Тези доповідей конференцій з теми "Deep syntax":
Blevins, Terra, Omer Levy, and Luke Zettlemoyer. "Deep RNNs Encode Soft Hierarchical Syntax." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2003.
Novák, Michal, Anna Nedoluzhko, and Zdeněk Žabokrtský. "Projection-based Coreference Resolution Using Deep Syntax." In Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-1508.
Novák, Václav. "On distance between deep syntax and semantic representation." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1641991.1642001.
Fei, Hao, Yafeng Ren, and Donghong Ji. "Improving Text Understanding via Deep Syntax-Semantics Communication." In Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.8.
Novák, Michal, Dieke Oele, and Gertjan van Noord. "Comparison of Coreference Resolvers for Deep Syntax Translation." In Proceedings of the Second Workshop on Discourse in Machine Translation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-2502.
Strubell, Emma, and Andrew McCallum. "Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?" In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-2904.
Shin, MyungJae, Joongheon Kim, Aziz Mohaisen, Jaebok Park, and Kyung Hee Lee. "Neural Network Syntax Analyzer for Embedded Standardized Deep Learning." In MobiSys '18: The 16th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3212725.3212727.
Feng, Hantao, Xiaotong Fu, Hongyu Sun, He Wang, and Yuqing Zhang. "Efficient Vulnerability Detection based on abstract syntax tree and Deep Learning." In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2020. http://dx.doi.org/10.1109/infocomwkshps50562.2020.9163061.
Rodriguez, Lino. "Deep Genetic Programming." In LatinX in AI at International Conference on Machine Learning 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019061512.
Wu, Bowen, Haoyang Huang, Zongsheng Wang, Qihang Feng, Jingsong Yu, and Baoxun Wang. "Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior." In Proceedings of the 2nd Workshop on Machine Reading for Question Answering. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5807.