Literatura científica selecionada sobre o tema "Deep semantic parsing"
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Artigos de revistas sobre o assunto "Deep semantic parsing"
Laukaitis, Algirdas, Egidijus Ostašius e Darius Plikynas. "Deep Semantic Parsing with Upper Ontologies". Applied Sciences 11, n.º 20 (11 de outubro de 2021): 9423. http://dx.doi.org/10.3390/app11209423.
Texto completo da fonteBALLESTEROS, MIGUEL, BERND BOHNET, SIMON MILLE e LEO WANNER. "Data-driven deep-syntactic dependency parsing". Natural Language Engineering 22, n.º 6 (18 de agosto de 2015): 939–74. http://dx.doi.org/10.1017/s1351324915000285.
Texto completo da fonteLuo, Ling, Dingyu Xue e Xinglong Feng. "EHANet: An Effective Hierarchical Aggregation Network for Face Parsing". Applied Sciences 10, n.º 9 (30 de abril de 2020): 3135. http://dx.doi.org/10.3390/app10093135.
Texto completo da fonteAbdelaziz, Ibrahim, Srinivas Ravishankar, Pavan Kapanipathi, Salim Roukos e Alexander Gray. "A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 18 (18 de maio de 2021): 15985–87. http://dx.doi.org/10.1609/aaai.v35i18.17988.
Texto completo da fonteHuang, Lili, Jiefeng Peng, Ruimao Zhang, Guanbin Li e Liang Lin. "Learning deep representations for semantic image parsing: a comprehensive overview". Frontiers of Computer Science 12, n.º 5 (30 de agosto de 2018): 840–57. http://dx.doi.org/10.1007/s11704-018-7195-8.
Texto completo da fonteFernández-Martínez, Nicolás José, e Pamela Faber. "Who stole what from whom?" Languages in Contrast 20, n.º 1 (5 de junho de 2019): 107–40. http://dx.doi.org/10.1075/lic.19002.fer.
Texto completo da fonteZhao, H., X. Zhang e C. Kit. "Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection". Journal of Artificial Intelligence Research 46 (20 de fevereiro de 2013): 203–33. http://dx.doi.org/10.1613/jair.3717.
Texto completo da fonteZhang, Xun, Yantao Du, Weiwei Sun e Xiaojun Wan. "Transition-Based Parsing for Deep Dependency Structures". Computational Linguistics 42, n.º 3 (setembro de 2016): 353–89. http://dx.doi.org/10.1162/coli_a_00252.
Texto completo da fonteZhou, Fan, Enbo Huang, Zhuo Su e Ruomei Wang. "Multiscale Meets Spatial Awareness: An Efficient Attention Guidance Network for Human Parsing". Mathematical Problems in Engineering 2020 (16 de outubro de 2020): 1–12. http://dx.doi.org/10.1155/2020/5794283.
Texto completo da fonteYang, Haitong, Tao Zhuang e Chengqing Zong. "Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks". Transactions of the Association for Computational Linguistics 3 (dezembro de 2015): 271–82. http://dx.doi.org/10.1162/tacl_a_00138.
Texto completo da fonteTeses / dissertações sobre o assunto "Deep semantic parsing"
He, Haoyu. "Deep learning based human parsing". Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24262.
Texto completo da fonteBillingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.
Texto completo da fonteXiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing". Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268/document.
Texto completo da fonteOur goal in this thesis is to build a system that answers a natural language question (NL) by representing its semantics as a logical form (LF) and then computing the answer by executing the LF over a knowledge base. The core part of such a system is the semantic parser that maps questions to logical forms. Our focus is how to build high-performance semantic parsers by learning from (NL, LF) pairs. We propose to combine recurrent neural networks (RNNs) with symbolic prior knowledge expressed through context-free grammars (CFGs) and automata. By integrating CFGs over LFs into the RNN training and inference processes, we guarantee that the generated logical forms are well-formed; by integrating, through weighted automata, prior knowledge over the presence of certain entities in the LF, we further enhance the performance of our models. Experimentally, we show that our approach achieves better performance than previous semantic parsers not using neural networks as well as RNNs not informed by such prior knowledge
Roxbo, Daniel. "A Detailed Analysis of Semantic Dependency Parsing with Deep Neural Networks". Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156831.
Texto completo da fonteBuys, Jan Moolman. "Incremental generative models for syntactic and semantic natural language processing". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:a9a7b5cf-3bb1-4e08-b109-de06bf387d1d.
Texto completo da fonteKočiský, Tomáš. "Deep learning for reading and understanding language". Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:cc45e366-cdd8-495b-af42-dfd726700ff0.
Texto completo da fonteKurita, Shuhei. "Neural Approaches for Syntactic and Semantic Analysis". Kyoto University, 2019. http://hdl.handle.net/2433/242436.
Texto completo da fonteMarzinotto, Gabriel. "Semantic frame based analysis using machine learning techniques : improving the cross-domain generalization of semantic parsers". Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0483.
Texto completo da fonteMaking semantic parsers robust to lexical and stylistic variations is a real challenge with many industrial applications. Nowadays, semantic parsing requires the usage of domain-specific training corpora to ensure acceptable performances on a given domain. Transfer learning techniques are widely studied and adopted when addressing this lack of robustness, and the most common strategy is the usage of pre-trained word representations. However, the best parsers still show significant performance degradation under domain shift, evidencing the need for supplementary transfer learning strategies to achieve robustness. This work proposes a new benchmark to study the domain dependence problem in semantic parsing. We use this bench to evaluate classical transfer learning techniques and to propose and evaluate new techniques based on adversarial learning. All these techniques are tested on state-of-the-art semantic parsers. We claim that adversarial learning approaches can improve the generalization capacities of models. We test this hypothesis on different semantic representation schemes, languages and corpora, providing experimental results to support our hypothesis
Tang, Anfu. "Leveraging linguistic and semantic information for relation extraction from domain-specific texts". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG081.
Texto completo da fonteThis thesis aims to extract relations from scientific documents in the biomedical domain, i.e. transform unstructured texts into structured data that is machine-readable. As a task in the domain of Natural Language Processing (NLP), the extraction of semantic relations between textual entities makes explicit and formalizes the underlying structures. Current state-of-the-art methods rely on supervised learning, more specifically the fine-tuning of pre-trained language models such as BERT. Supervised learning requires a large amount of examples that are expensive to produce, especially in specific domains such as the biomedical domain. BERT variants such as PubMedBERT have been successful on NLP tasks involving biomedical texts. We hypothesize that injecting external information such as syntactic information or factual knowledge into such BERT variants can compensate for the reduced number of annotated training data. To this end, this thesis consists of proposing several neural architectures based on PubMedBERT that exploit linguistic information obtained by syntactic parsers or domain knowledge from knowledge bases
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.
Texto completo da fonteNowadays, 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
Livros sobre o assunto "Deep semantic parsing"
Reckman, Hilletje Gezina Bouwke. Flat but not shallow: Towards flatter representations in deep semantic parsing for precise and feasible inferencing : proefschrift. Utrecht: LOT, 2009.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Deep semantic parsing"
Gołuchowski, Konrad, e Adam Przepiórkowski. "Semantic Role Labelling without Deep Syntactic Parsing". In Advances in Natural Language Processing, 192–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33983-7_19.
Texto completo da fonteJayasinghe, Ishadi, e Surangika Ranathunga. "Two-Step Memory Networks for Deep Semantic Parsing of Geometry Word Problems". In SOFSEM 2020: Theory and Practice of Computer Science, 676–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38919-2_57.
Texto completo da fonteRavikiran, Pichika, e Midhun Chakkaravarthy. "Improved Efficiency of Semantic Segmentation using Pyramid Scene Parsing Deep Learning Network Method". In Intelligent Systems and Sustainable Computing, 175–81. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0011-2_16.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Deep semantic parsing"
Grefenstette, Edward, Phil Blunsom, Nando de Freitas e Karl Moritz Hermann. "A Deep Architecture for Semantic Parsing". In Proceedings of the ACL 2014 Workshop on Semantic Parsing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-2405.
Texto completo da fonteDuong, Long, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen e Mark Johnson. "Active learning for deep semantic parsing". 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/p18-2008.
Texto completo da fonteSharma, Abhishek, Oncel Tuzel e David W. Jacobs. "Deep hierarchical parsing for semantic segmentation". In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298651.
Texto completo da fontePeng, Hao, Sam Thomson e Noah A. Smith. "Deep Multitask Learning for Semantic Dependency Parsing". In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1186.
Texto completo da fonteLiu, Ziwei, Xiaoxiao Li, Ping Luo, Chen-Change Loy e Xiaoou Tang. "Semantic Image Segmentation via Deep Parsing Network". In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.162.
Texto completo da fonteXiao, Chunyang, Christoph Teichmann e Konstantine Arkoudas. "Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing". In Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-3902.
Texto completo da fonteLiu, Hantang, Jialiang Zhang, Jianke Zhu e Steven C. H. Hoi. "DeepFacade: A Deep Learning Approach to Facade Parsing". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/320.
Texto completo da fontePackard, Woodley, Emily M. Bender, Jonathon Read, Stephan Oepen e Rebecca Dridan. "Simple Negation Scope Resolution through Deep Parsing: A Semantic Solution to a Semantic Problem". In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-1007.
Texto completo da fonteJain, Shubham, Amy de Buitleir e Enda Fallon. "A Framework for Adaptive Deep Reinforcement Semantic Parsing of Unstructured Data". In 2021 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2021. http://dx.doi.org/10.1109/ictc52510.2021.9620904.
Texto completo da fonteLiao, Zhihua, e Yan Xie. "A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing". In RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning. Incoma Ltd. Shoumen, Bulgaria, 2017. http://dx.doi.org/10.26615/978-954-452-049-6_059.
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