Thematische Bibliographien / Deep semantic parsing

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Auswahl der wissenschaftlichen Literatur zum Thema „Deep semantic parsing“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Deep semantic parsing"

Laukaitis, Algirdas, Egidijus Ostašius, and Darius Plikynas. "Deep Semantic Parsing with Upper Ontologies." Applied Sciences 11, no. 20 (2021): 9423. http://dx.doi.org/10.3390/app11209423.

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This paper presents a new method for semantic parsing with upper ontologies using FrameNet annotations and BERT-based sentence context distributed representations. The proposed method leverages WordNet upper ontology mapping and PropBank-style semantic role labeling and it is designed for long text parsing. Given a PropBank, FrameNet and WordNet-labeled corpus, a model is proposed that annotates the set of semantic roles with upper ontology concept names. These annotations are used for the identification of predicates and arguments that are relevant for virtual reality simulators in a 3D world

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BALLESTEROS, MIGUEL, BERND BOHNET, SIMON MILLE, and LEO WANNER. "Data-driven deep-syntactic dependency parsing." Natural Language Engineering 22, no. 6 (2015): 939–74. http://dx.doi.org/10.1017/s1351324915000285.

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Abstract‘Deep-syntactic’ dependency structures that capture the argumentative, attributive and coordinative relations between full words of a sentence have a great potential for a number of NLP-applications. The abstraction degree of these structures is in between the output of a syntactic dependency parser (connected trees defined over all words of a sentence and language-specific grammatical functions) and the output of a semantic parser (forests of trees defined over individual lexemes or phrasal chunks and abstract semantic role labels which capture the frame structures of predicative elem

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Luo, Ling, Dingyu Xue, and Xinglong Feng. "EHANet: An Effective Hierarchical Aggregation Network for Face Parsing." Applied Sciences 10, no. 9 (2020): 3135. http://dx.doi.org/10.3390/app10093135.

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In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network c

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Abdelaziz, Ibrahim, Srinivas Ravishankar, Pavan Kapanipathi, Salim Roukos, and Alexander Gray. "A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15985–87. http://dx.doi.org/10.1609/aaai.v35i18.17988.

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Knowledge Base Question Answering (KBQA) is a task where existing techniques have faced significant challenges, such as the need for complex question understanding, reasoning, and large training datasets. In this work, we demonstrate Deep Thinking Question Answering (DTQA), a semantic parsing and reasoning-based KBQA system. DTQA (1) integrates multiple, reusable modules that are trained specifically for their individual tasks (e.g. semantic parsing, entity linking, and relationship linking), eliminating the need for end-to-end KBQA training data; (2) leverages semantic parsing and a reasoner

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Huang, Lili, Jiefeng Peng, Ruimao Zhang, Guanbin Li, and Liang Lin. "Learning deep representations for semantic image parsing: a comprehensive overview." Frontiers of Computer Science 12, no. 5 (2018): 840–57. http://dx.doi.org/10.1007/s11704-018-7195-8.

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Papakostas, Christos, Christos Troussas, Akrivi Krouska, and Cleo Sgouropoulou. "A Hybrid Neuro-Symbolic Pipeline for Coreference Resolution and AMR-Based Semantic Parsing." Information 16, no. 7 (2025): 529. https://doi.org/10.3390/info16070529.

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Large Language Models (LLMs) have transformed Natural Language Processing (NLP), yet they continue to struggle with deep semantic understanding, particularly in tasks like coreference resolution and structured semantic inference. This study presents a hybrid neuro-symbolic pipeline that combines transformer-based contextual encoding with symbolic coreference resolution and Abstract Meaning Representation (AMR) parsing to improve natural language understanding. The pipeline resolves referential ambiguity using a rule-based coreference module and generates semantic graphs from disambiguated inpu

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Zhao, H., X. Zhang, and C. Kit. "Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection." Journal of Artificial Intelligence Research 46 (February 20, 2013): 203–33. http://dx.doi.org/10.1613/jair.3717.

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Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series of submodels each specialized for a specific subtask, the one presented in this article integrates everything into one model, in hopes of achieving desirable integrity and practicality for real applications while maintaining a competitive performance. This integrative ap

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Fernández-Martínez, Nicolás José, and Pamela Faber. "Who stole what from whom?" Languages in Contrast 20, no. 1 (2019): 107–40. http://dx.doi.org/10.1075/lic.19002.fer.

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Abstract Drawing on the Lexical Grammar Model, Frame Semantics and Corpus Pattern Analysis, we analyze and contrast verbs of stealing in English and Spanish from a lexico-semantic perspective. This involves looking at the lexical collocates and their corresponding semantic categories that fill the argument slots of verbs of stealing. Our corpus search is performed with the Word Sketch tool on Sketch Engine. To the best of our knowledge, no study has yet taken advantage of the Word Sketch tool in the study of the selection preferences of verbs of stealing, let alone a semantic, cross-linguistic

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Zhang, Xun, Yantao Du, Weiwei Sun, and Xiaojun Wan. "Transition-Based Parsing for Deep Dependency Structures." Computational Linguistics 42, no. 3 (2016): 353–89. http://dx.doi.org/10.1162/coli_a_00252.

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Derivations under different grammar formalisms allow extraction of various dependency structures. Particularly, bilexical deep dependency structures beyond surface tree representation can be derived from linguistic analysis grounded by CCG, LFG, and HPSG. Traditionally, these dependency structures are obtained as a by-product of grammar-guided parsers. In this article, we study the alternative data-driven, transition-based approach, which has achieved great success for tree parsing, to build general dependency graphs. We integrate existing tree parsing techniques and present two new transition

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Zhou, Fan, Enbo Huang, Zhuo Su, and Ruomei Wang. "Multiscale Meets Spatial Awareness: An Efficient Attention Guidance Network for Human Parsing." Mathematical Problems in Engineering 2020 (October 16, 2020): 1–12. http://dx.doi.org/10.1155/2020/5794283.

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Human parsing, which aims at resolving human body and clothes into semantic part regions from an human image, is a fundamental task in human-centric analysis. Recently, the approaches for human parsing based on deep convolutional neural networks (DCNNs) have made significant progress. However, hierarchically exploiting multiscale and spatial contexts as convolutional features is still a hurdle to overcome. In order to boost the scale and spatial awareness of a DCNN, we propose two effective structures, named “Attention SPP and Attention RefineNet,” to form a Mutual Attention operation, to expl

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Dissertationen zum Thema "Deep semantic parsing"

He, Haoyu. "Deep learning based human parsing." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24262.

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Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. Although prior works have made significant progress by introducing large-scale datasets and deep learning to solve the problem, there are still two challenges remain unsolved. Firstly, to better exploit the existing parsing annotations, prior methods learn a knowledge-sharing mechanism to improve semantic structures in cross-dataset human parsing. However, the modeling for such mechanism remains inefficient for not considering classes' granularity difference in dif

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Billingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.

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Deep machine learning has enjoyed recent success in vision and speech-to-text tasks, using deep multi-layered neural networks. They have obtained remarkable results particularly where the internal representation of the task is unclear. In parsing, where the structure of syntax is well studied and understood from linguistics, neural networks have so far not performed so well. State-of-the-art parsers use a tree-based graphical model that requires a large number of equivalent classes to represent each parse node and its phrase label. A recursive neural network (RNN) parser has been developed th

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Xiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268/document.

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Notre but dans cette thèse est de construire un système qui réponde à une question en langue naturelle (NL) en représentant sa sémantique comme une forme logique (LF) et ensuite en calculant une réponse en exécutant cette LF sur une base de connaissances. La partie centrale d'un tel système est l'analyseur sémantique qui transforme les questions en formes logiques. Notre objectif est de construire des analyseurs sémantiques performants en apprenant à partir de paires (NL, LF). Nous proposons de combiner des réseaux neuronaux récurrents (RNN) avec des connaissances préalables symboliques exprim

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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.

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The use of Long Short Term Memory (LSTM) networks continues to yield better results in natural language processing tasks. One area which recently has seen significant improvements is semantic dependency parsing, where the current state-of-the-art model uses a multilayer LSTM combined with an attention-based scoring function to predict the dependencies. In this thesis the state of the art model is first replicated and then extended to include features based on syntactical trees, which was found to be useful in a similar model. In addition, the effect of part-of-speech tags is studied. The repli

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Xiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268.

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Buys, 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.

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This thesis investigates the role of linguistically-motivated generative models of syntax and semantic structure in natural language processing (NLP). Syntactic well-formedness is crucial in language generation, but most statistical models do not account for the hierarchical structure of sentences. Many applications exhibiting natural language understanding rely on structured semantic representations to enable querying, inference and reasoning. Yet most semantic parsers produce domain-specific or inadequately expressive representations. We propose a series of generative transition-based models

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Kočiský, 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.

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This thesis presents novel tasks and deep learning methods for machine reading comprehension and question answering with the goal of achieving natural language understanding. First, we consider a semantic parsing task where the model understands sentences and translates them into a logical form or instructions. We present a novel semi-supervised sequential autoencoder that considers language as a discrete sequential latent variable and semantic parses as the observations. This model allows us to leverage synthetically generated unpaired logical forms, and thereby alleviate the lack of supervis

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Kurita, Shuhei. "Neural Approaches for Syntactic and Semantic Analysis." Kyoto University, 2019. http://hdl.handle.net/2433/242436.

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Marzinotto, 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.

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Rendre les analyseurs sémantiques robustes aux variations lexicales et stylistiques est un véritable défi pour de nombreuses applications industrielles. De nos jours, l'analyse sémantique nécessite de corpus annotés spécifiques à chaque domaine afin de garantir des performances acceptables. Les techniques d'apprenti-ssage par transfert sont largement étudiées et adoptées pour résoudre ce problème de manque de robustesse et la stratégie la plus courante consiste à utiliser des représentations de mots pré-formés. Cependant, les meilleurs analyseurs montrent toujours une dégradation significative

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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.

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Cette thèse a pour objet l'extraction d'informations relationnelles à partir de documents scientifiques biomédicaux, c'est-à-dire la transformation de texte non structuré en information structurée exploitable par une machine. En tant que tâche dans le domaine du traitement automatique des langues (TAL), l'extraction de relations sémantiques spécialisées entre entités textuelles rend explicite et formalise les structures sous-jacentes. Les méthodes actuelles à l'état de l'art s'appuient sur de l'apprentissage supervisé, plus spécifiquement l'ajustement de modèles de langue pré-entraînés comme B

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Bücher zum Thema "Deep semantic parsing"

Reckman, Hilletje Gezina Bouwke. Flat but not shallow: Towards flatter representations in deep semantic parsing for precise and feasible inferencing : proefschrift. LOT, 2009.

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Buchteile zum Thema "Deep semantic parsing"

Gołuchowski, Konrad, and Adam Przepiórkowski. "Semantic Role Labelling without Deep Syntactic Parsing." In Advances in Natural Language Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33983-7_19.

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Jayasinghe, Ishadi, and Surangika Ranathunga. "Two-Step Memory Networks for Deep Semantic Parsing of Geometry Word Problems." In SOFSEM 2020: Theory and Practice of Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38919-2_57.

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Ravikiran, Pichika, and Midhun Chakkaravarthy. "Improved Efficiency of Semantic Segmentation using Pyramid Scene Parsing Deep Learning Network Method." In Intelligent Systems and Sustainable Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0011-2_16.

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Di Sciullo Anna Maria. "Asymmetry Based Parsing and Semantic Compositionality." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-800-6-190.

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The operations of the Language Faculty generate the asymmetrical structure of linguistic expressions, which provides the spine for their compositional semantics. Neuroimaging results support the structure dependent sensitivity of the brain to language processing. Psycholinguistics results on language development in the child show that language learning is structure dependent and not based on extensive training on data sets. We contrast this view of language computation and learning to Deep Learning, which is claimed to provide the best solutions to many problems in image recognition, speech re

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Brighi Raffaella, Lesmo Leonardo, Mazzei Alessandro, Palmirani Monica, and Radicioni Daniele P. "Towards Semantic Interpretation of Legal Modifications through Deep Syntactic Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-952-3-202.

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We are concerned with the automatic semantic interpretation of legal modificatory provisions. We propose a novel approach which pairs deep syntactic parsing and a fine-grained taxonomy of legal modifications. Although still in a developmental stage, the implemented system can be used to annotate with meta-information modificatory provisions of NormaInRete documents.

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Hartrumpf Sven. "Semantic Decomposition for Question Answering." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-891-5-313.

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In this paper, we develop and evaluate methods for decomposing complex questions for a question answering system to less complex questions. This aims at increasing the number of correct answers, especially in (deep) semantic question answering systems. For example, an event that occurs as a temporal restriction of a question can be queried for its date and the resulting answer can be substituted in the original question leading to a simpler, revised question. We present six decomposition classes, which are employed for annotating the 996 different German QA@CLEF questions from 2004 till 2008 a

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Konferenzberichte zum Thema "Deep semantic parsing"

Gu, Donghu, and Haiyan Tan. "Deep Reinforcement Learning Based Chat Bot Using Semantic Parsing Method." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691264.

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Abdullah, Adnan, Titon Barua, Reagan Tibbetts, Zijie Chen, Md Jahidul Islam, and Ioannis Rekleitis. "CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611543.

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Narazaki, Yasutaka, Jiawei Xu, and Mingyu Shi. "Deep learning-based semantic parsing and geometry extraction of bridge point cloud data toward fully automated Scan-to-BIM." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.1450.

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<p>This research investigates a robust instance segmentation approach for noisy and imperfect bridge point cloud data collected in the field to facilitate the automated as-built structural modeling. First, a parametric generator of bridges, termed Random Bridge Generator (RBG), is developed to generate photo-realistic synthetic models of different types of bridges randomly and automatically. The synthetic environments are then used to obtain realistic point cloud data with point-wise annotations of structural components. Finally, unsupervised domain adaptation methodology is developed to

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Grefenstette, Edward, Phil Blunsom, Nando de Freitas, and Karl Moritz Hermann. "A Deep Architecture for Semantic Parsing." In Proceedings of the ACL 2014 Workshop on Semantic Parsing. Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-2405.

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Duong, Long, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, and 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). Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2008.

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Sharma, Abhishek, Oncel Tuzel, and 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.

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Peng, Hao, Sam Thomson, and 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). Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1186.

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Liu, Ziwei, Xiaoxiao Li, Ping Luo, Chen-Change Loy, and 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.

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Xiao, Chunyang, Christoph Teichmann, and Konstantine Arkoudas. "Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing." In Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-3902.

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Liu, Hantang, Jialiang Zhang, Jianke Zhu, and Steven C. H. Hoi. "DeepFacade: A Deep Learning Approach to Facade Parsing." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/320.

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The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using boun

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