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Thematische Bibliographien / Deep semantic parsing / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Deep semantic parsing“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

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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Yang, Haitong, Tao Zhuang, and Chengqing Zong. "Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks." Transactions of the Association for Computational Linguistics 3 (December 2015): 271–82. http://dx.doi.org/10.1162/tacl_a_00138.

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In current systems for syntactic and semantic dependency parsing, people usually define a very high-dimensional feature space to achieve good performance. But these systems often suffer severe performance drops on out-of-domain test data due to the diversity of features of different domains. This paper focuses on how to relieve this domain adaptation problem with the help of unlabeled target domain data. We propose a deep learning method to adapt both syntactic and semantic parsers. With additional unlabeled target domain data, our method can learn a latent feature representation (LFR) that is

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Xin, Peng, and Li Qiujun. "Semantic Dependency Graph Parsing of Financial Domain Questions Based on Deep Learning." Journal of Physics: Conference Series 1453 (January 2020): 012058. http://dx.doi.org/10.1088/1742-6596/1453/1/012058.

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Yao, Xuchen, Gosse Bouma, and Yi Zhang. "Semantics-based Question Generation and Implementation." Dialogue & Discourse 3, no. 2 (2012): 11–42. http://dx.doi.org/10.5087/dad.2012.202.

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This paper presents a question generation system based on the approach of semantic rewriting. The state-of-the-art deep linguistic parsing and generation tools are employed to convert (back and forth) between the natural language sentences and their meaning representations in the form of Minimal Recursion Semantics (MRS). By carefully operating on the semantic structures, we show a principled way of generating questions without ad-hoc manipulation of the syntactic structures. Based on the (partial) understanding of the sentence meaning, the system generates questions which are semantically gro

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Zhou, Wujie, Shaohua Dong, Caie Xu, and Yaguan Qian. "Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3571–79. http://dx.doi.org/10.1609/aaai.v36i3.20269.

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RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed

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Yin, Zeyu, Jinsong Shao, Muhammad Jawad Hussain, et al. "DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN." Applied Sciences 13, no. 1 (2022): 354. http://dx.doi.org/10.3390/app13010354.

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Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies’ good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DP

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Bauer, Daniel. "Understanding Descriptions of Visual Scenes Using Graph Grammars." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1656–57. http://dx.doi.org/10.1609/aaai.v27i1.8498.

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Automatic generation of 3D scenes from descriptions has applications in communication, education, and entertainment, but requires deep understanding of the input text. I propose thesis work on language understanding using graph-based meaning representations that can be decomposed into primitive spatial relations. The techniques used for analyzing text and transforming it into a scene representation are based on context-free graph grammars. The thesis develops methods for semantic parsing with graphs, acquisition of graph grammars, and satisfaction of spatial and world-knowledge constraints dur

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Zhao, Ruilin, Yanbing Xue, Jing Cai, and Zan Gao. "Parsing human image by fusing semantic and spatial features: A deep learning approach." Information Processing & Management 57, no. 6 (2020): 102306. http://dx.doi.org/10.1016/j.ipm.2020.102306.

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Ndongala, Nathan Manzambi. "Light RAT-SQL: A RAT-SQL with More Abstraction and Less Embedding of Pre-existing Relations." TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH 10, no. 2 (2023): 1–11. http://dx.doi.org/10.21522/tijar.2014.10.02.art001.

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RAT-SQL is among the popular framework used in the Text-To-SQL challenges for jointly encoding the database relations and questions in a way to improve the semantic parser. In this work, we propose a light version of the RAT-SQL where we dramatically reduced the number of the preexisting relations from 55 to 7 (Light RAT-SQL-7) while preserving the same parsing accuracy. To ensure the effectiveness of our approach, we trained a Light RAT-SQL-2, (with 2 embeddings) to show that there is a statistically significant difference between RAT-SQL and Light RAT-SQL-2 while Light RAT-SQL-7 can compete

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Qian, Rui, Yunchao Wei, Honghui Shi, Jiachen Li, Jiaying Liu, and Thomas Huang. "Weakly Supervised Scene Parsing with Point-Based Distance Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8843–50. http://dx.doi.org/10.1609/aaai.v33i01.33018843.

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Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature represent

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Li, Xiangtai, Houlong Zhao, Lei Han, Yunhai Tong, Shaohua Tan, and Kuiyuan Yang. "Gated Fully Fusion for Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11418–25. http://dx.doi.org/10.1609/aaai.v34i07.6805.

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Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features. Unfortunately, simply combining multi-level features suffers from

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Qi, Binting, Hanming Zhai, Yunyang Bu, Zhuxuan Han, and Fanliang Bu. "Cyber Violence Text Classification Model Based on Graph Convolutional Networks and Syntactic Parsing." Frontiers in Computing and Intelligent Systems 11, no. 1 (2025): 29–34. https://doi.org/10.54097/xq0zek31.

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There are problems such as semantic sparsity and incomplete context in cyber violence texts in social media, and the current research on the refinement and classification of cyber violence texts is insufficient. This paper constructs a text classification model based on graph neural networks to improve the fine-grained classification effect of multi category cyber violence texts in social media. Introducing dependency relationships into BERT through syntactic analysis for deep semantic representation learning enhances the model's contextual understanding ability and better captures fine-graine

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OHTA, Tomoko. "Semantic retrieval for the accurate identification of relational concepts based on deep syntactic parsing." Journal of Information Processing and Management 49, no. 10 (2007): 555–63. http://dx.doi.org/10.1241/johokanri.49.555.

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Dai, Hongming. "LinGAN: an Advanced Model for Code Generating based on Linformer." Journal of Physics: Conference Series 2082, no. 1 (2021): 012019. http://dx.doi.org/10.1088/1742-6596/2082/1/012019.

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Abstract Parsing natural language to corresponding programming language attracts much attention in recent years. Natural Language to SQL(NL2SQL) widely appears in numerous practical Internet applications. Previous solution was to convert the input as a heterogeneous graph which failed to learn good word representation in question utterance. In this paper, we propose a Relation-Aware framework named LinGAN, which has powerful semantic parsing abilities and can jointly encode the question utterance and syntax information of the object language. We also propose the pre-norm residual shrinkage uni

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Frisoni, Giacomo, Paolo Italiani, Stefano Salvatori, and Gianluca Moro. "Cogito Ergo Summ: Abstractive Summarization of Biomedical Papers via Semantic Parsing Graphs and Consistency Rewards." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12781–89. http://dx.doi.org/10.1609/aaai.v37i11.26503.

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The automatic synthesis of biomedical publications catalyzes a profound research interest elicited by literature congestion. Current sequence-to-sequence models mainly rely on the lexical surface and seldom consider the deep semantic interconnections between the entities mentioned in the source document. Such superficiality translates into fabricated, poorly informative, redundant, and near-extractive summaries that severely restrict their real-world application in biomedicine, where the specialized jargon and the convoluted facts further emphasize task complexity. To fill this gap, we argue t

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Quispe, Rodolfo, and Helio Pedrini. "Improved person re-identification based on saliency and semantic parsing with deep neural network models." Image and Vision Computing 92 (December 2019): 103809. http://dx.doi.org/10.1016/j.imavis.2019.07.009.

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Xia, Qingrong, Zhenghua Li, Min Zhang, et al. "Syntax-Aware Neural Semantic Role Labeling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7305–13. http://dx.doi.org/10.1609/aaai.v33i01.33017305.

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Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are benefic

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Zou, Nan, Zhiyu Xiang, Yiman Chen, Shuya Chen, and Chengyu Qiao. "Simultaneous Semantic Segmentation and Depth Completion with Constraint of Boundary." Sensors 20, no. 3 (2020): 635. http://dx.doi.org/10.3390/s20030635.

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As the core task of scene understanding, semantic segmentation and depth completion play a vital role in lots of applications such as robot navigation, AR/VR and autonomous driving. They are responsible for parsing scenes from the angle of semantics and geometry, respectively. While great progress has been made in both tasks through deep learning technologies, few works have been done on building a joint model by deeply exploring the inner relationship of the above tasks. In this paper, semantic segmentation and depth completion are jointly considered under a multi-task learning framework. By

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Staykova, Kamenka, Petya Osenova, and Kiril Simov. "New Applications of “Ontology-to-Text Relation” Strategy for Bulgarian Language." Cybernetics and Information Technologies 12, no. 4 (2012): 43–51. http://dx.doi.org/10.2478/cait-2012-0029.

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Abstract The paper presents new applications of the Ontology-to-Text Relation Strategy to Bulgarian Iconographic Domain. First the strategy itself is discussed within the triple ontology-terminological lexicon-annotation grammars, then - the related works. Also, the specifics of the semantic annotation and evaluation over iconographic data are presented. A family of domain ontologies over the iconographic domain are created and used. The evaluation against a gold standard shows that this strategy is good enough for more precise, but shallow results, and can be supported further by deep parsing

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Mohit, Thodupunuri. "NLP vs. Generative AI: A Comparative Study with Insights into Other Leading AI Technologies." Journal of Scientific and Engineering Research 8, no. 3 (2021): 285–93. https://doi.org/10.5281/zenodo.15223036.

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Natural Language Processing (NLP) and Generative AI are two key fields in artificial intelligence. NLP decodes language using tokenization and semantic parsing. It uses deep learning and neural networks to understand text. Meanwhile, Generative AI creates new content with transformers and autoregressive models. It produces text, code, and multimedia. Both use advanced machine learning techniques. Moreover, NLP boosts customer service and data analysis. Likewise, Generative AI drives creative writing and code generation. However, both face challenges. NLP struggles with contextual ambiguity. Ge

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Lee, Changui, Hoyeon Cho, and Seojeong Lee. "Analysis of Bi-LSTM CRF Series Models for Semantic Classification of NAVTEX Navigational Safety Messages." Journal of Marine Science and Engineering 12, no. 9 (2024): 1518. http://dx.doi.org/10.3390/jmse12091518.

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NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX

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Dr.Wei Meng, Dr Wei Meng, and Dr Xiaoyin Zhang Dr.Xiaoyin Zhang. "BERT-based semantic analysis and fuzzy language parsing: interpreting the prophecies and historical event associations in the 40th Xiang of Tui Bei Tu." International Journal of Advances in Engineering and Management 6, no. 10 (2024): 420–28. https://doi.org/10.35629/5252-0610420428.

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Based on the model of BERT, prophecies and homilies in the 40th Xiang of the Tui Bei Tu are analyzed semantically in this study for a possible connection in understanding historical events during the Qing Dynasty. It integrates natural language processing techniques into the research methodology, together with fuzzy parsing of language. Further, it gives ways of applying deep learning models for the recognition of metaphors and polysemous phrases in text. It also carries out time-series analysis based on a historical event dataset. Results from the study indicate that metaphors in prophetic ph

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Ma, Sai, Weibing Wan, Zedong Yu, and Yuming Zhao. "EDET: Entity Descriptor Encoder of Transformer for Multi-Modal Knowledge Graph in Scene Parsing." Applied Sciences 13, no. 12 (2023): 7115. http://dx.doi.org/10.3390/app13127115.

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In scene parsing, the model is required to be able to process complex multi-modal data such as images and contexts in real scenes, and discover their implicit connections from objects existing in the scene. As a storage method that contains entity information and the relationship between entities, a knowledge graph can well express objects and the semantic relationship between objects in the scene. In this paper, a new multi-phase process was proposed to solve scene parsing tasks; first, a knowledge graph was used to align the multi-modal information and then the graph-based model generates re

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Aslantaş, Nuran, Tolga Bakırman, Mahmut Oğuz Selbesoğlu, and Bülent Bayram. "The Role of Ensemble Deep Learning for Building Extraction from VHR Imagery." International Journal of Engineering and Geosciences 10, no. 3 (2025): 352–63. https://doi.org/10.26833/ijeg.1587798.

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In modern geographical applications, the demand for up-to-date and accurate building maps is increasing, driven by essential needs in sustainable urban planning, sprawl monitoring, natural hazard mitigation, crisis management, smart city initiatives, and the establishment of climate-resilient urban environments. The unregulated growth in urbanization and settlement patterns poses multifaceted challenges, including ecological imbalances, loss of arable land, and increasing risk of drought. Leveraging recent technologies in remote sensing and artificial intelligence, particularly in the fields o

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Manzambi Ndongala, Nathan. "Topological Relation Aware Transformer." Texila International Journal of Academic Research 11, no. 1 (2024): 160–74. http://dx.doi.org/10.21522/tijar.2014.11.01.art015.

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We present a Topological Relation Aware Transformer (T-RAT), a specialized head transformer to open sets, an element of the topology τ generated by the set S, the set of all pre-existing relations between input tokens of the model. From this topological space (S, τ), we present the way to spread each open set to one head of our Transformer. T-RAT improves exact match accuracy in Text-To-SQL challenge (62.09%) without any enhancement of large language models compared to the baseline models RAT-SQL (57.2%) and Light RAT-SQL (60.25%). Keywords: Deep learning, Natural Language Processing, Neural S

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Wang, Yani. "Application of large language models based on knowledge graphs in question-answering systems: A review." Applied and Computational Engineering 71, no. 1 (2024): 78–82. http://dx.doi.org/10.54254/2755-2721/71/20241636.

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The integration of Knowledge Graphs (KGs) and Large Language Models (LLMs) is emerging as a transformative advancement in AI, particularly within Question Answering (QA) systems. Traditional QA systems, constrained by static knowledge bases, have struggled with multimodal queries and personalized responses. The deep integration of KGs and LLMs offers a novel approach, combining the structured, contextual understanding of KGs with the semantic parsing capabilities of LLMs. This review explores the methodologies, algorithms, datasets, and applications of KG-LLM integration in QA systems, highlig

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Golait, Dr Snehal. "Implementation of Text to Image using Diffusion Model." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34583.

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Text-to-image generation is a transformative field in artificial intelligence, aiming to bridge the semantic gap between textual descriptions and visual representations. This presents a comprehensive approach to tackle this challenging task. Leveraging the advancements in deep learning, natural language processing (NLP), and computer vision, this proposes a cutting-edge model for generating high-fidelity images from textual prompts. Trained on a vast and varied dataset of written descriptions and related images, this model combines an image decoder and a text encoder within a hierarchical fram

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Şahin, Gözde Gül. "To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP." Computational Linguistics 48, no. 1 (2022): 5–42. http://dx.doi.org/10.1162/coli_a_00425.

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Abstract Data-hungry deep neural networks have established themselves as the de facto standard for many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they still fall behind their statistical counterparts in low-resource scenarios. One methodology to counterattack this problem is text augmentation, that is, generating new synthetic training data points from existing data. Although NLP has recently witnessed several new textual augmentation techniques, the field still lacks a systematic performance analysis on a

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Costa, Marcus Vinícius Coelho Vieira da, Osmar Luiz Ferreira de Carvalho, Alex Gois Orlandi, et al. "Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation." Energies 14, no. 10 (2021): 2960. http://dx.doi.org/10.3390/en14102960.

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Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We co

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Tosic, D., S. Tuttas, L. Hoegner, and U. Stilla. "FUSION OF FEATURE BASED AND DEEP LEARNING METHODS FOR CLASSIFICATION OF MMS POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 235–42. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-235-2019.

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<p><strong>Abstract.</strong> This work proposes an approach for semantic classification of an outdoor-scene point cloud acquired with a high precision Mobile Mapping System (MMS), with major goal to contribute to the automatic creation of High Definition (HD) Maps. The automatic point labeling is achieved by utilizing the combination of a feature-based approach for semantic classification of point clouds and a deep learning approach for semantic segmentation of images. Both, point cloud data, as well as the data from a multi-camera system are used for gaining spatial informa

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Madhukar Jukanti. "Understanding Natural Language Processing (NLP) Techniques." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 1005–12. https://doi.org/10.32996/jcsts.2025.7.7.112.

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Natural Language Processing (NLP) is a transformative field that integrates computational intelligence with human language through sophisticated algorithms. This paper explores foundational mechanisms that enable machines to understand, translate, and infer human language across various applications. Key processing techniques include tokenization using compression-based subword segmentation and Named Entity Recognition (NER) employing BiLSTM-CNN architectures for high-accuracy entity tagging. Sentiment analysis utilizes convolutional neural networks and transformer-based encoders to extract nu

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Sun, Qingwei, Jiangang Chao, Wanhong Lin, et al. "Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes." Aerospace 11, no. 6 (2024): 496. http://dx.doi.org/10.3390/aerospace11060496.

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Few-shot segmentation (FSS) is a cutting-edge technology that can meet requirements using a small workload. With the development of China Aerospace Engineering, FSS plays a fundamental role in astronaut working scene (AWS) intelligent parsing. Although mainstream FSS methods have made considerable breakthroughs in natural data, they are not suitable for AWSs. AWSs are characterized by a similar foreground (FG) and background (BG), indistinguishable categories, and the strong influence of light, all of which place higher demands on FSS methods. We design a pixel-wise and class-wise network (PCN

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UZZAMAN, NAUSHAD, and JAMES F. ALLEN. "EVENT AND TEMPORAL EXPRESSION EXTRACTION FROM RAW TEXT: FIRST STEP TOWARDS A TEMPORALLY AWARE SYSTEM." International Journal of Semantic Computing 04, no. 04 (2010): 487–508. http://dx.doi.org/10.1142/s1793351x10001097.

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Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and use the temporal structure in other applications like textual entailment, question answering, visualization, or others. In this paper, we present a first step, a system for extracting events, event features, main events, temporal expressions and their normalized values from raw text. Our system is a combination of deep sem

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Pan, Qian, Maofang Gao, Pingbo Wu, Jingwen Yan, and Shilei Li. "A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images." Sensors 21, no. 19 (2021): 6540. http://dx.doi.org/10.3390/s21196540.

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Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the h

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Iacob, Radu Cristian Alexandru, Vlad Cristian Monea, Dan Rădulescu, Andrei-Florin Ceapă, Traian Rebedea, and Ștefan Trăușan-Matu. "AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges." Mathematics 8, no. 11 (2020): 1995. http://dx.doi.org/10.3390/math8111995.

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While semantic parsing has been an important problem in natural language processing for decades, recent years have seen a wide interest in automatic generation of code from text. We propose an alternative problem to code generation: labelling the algorithmic solution for programming challenges. While this may seem an easier task, we highlight that current deep learning techniques are still far from offering a reliable solution. The contributions of the paper are twofold. First, we propose a large multi-modal dataset of text and code pairs consisting of algorithmic challenges and their solution

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H M, Chandana. "Career Compass: Navigating to the Right Job with Machine Learning Precision." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49618.

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Abstract - Job recommendation systems are critical tools in bridging the gap between job seekers and employers by automatically suggesting suitable opportunities. This paper explores a resume- and skill-based job recommendation system using a hybrid approach combining machine learning, deep learning, and NLP techniques. The proposed system parses user resumes to extract structured skill sets and work experience using NLP. Then, it applies transformer-based models like BERT for contextual embedding of resume and job descriptions to calculate similarity scores. Additionally, knowledge graphs are

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Panboonyuen, Teerapong, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern, and Peerapon Vateekul. "Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images." Remote Sensing 13, no. 24 (2021): 5100. http://dx.doi.org/10.3390/rs13245100.

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Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature p

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Li, Rui, Shili Shu, Shunli Wang, Yang Liu, Yanhao Li, and Mingjun Peng. "DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields." Entropy 25, no. 10 (2023): 1444. http://dx.doi.org/10.3390/e25101444.

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The rapid development of information technology has made the amount of information in massive texts far exceed human intuitive cognition, and dependency parsing can effectively deal with information overload. In the background of domain specialization, the migration and application of syntactic treebanks and the speed improvement in syntactic analysis models become the key to the efficiency of syntactic analysis. To realize domain migration of syntactic tree library and improve the speed of text parsing, this paper proposes a novel approach—the Double-Array Trie and Multi-threading (DAT-MT) ac

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Zhang, Zeyu, Honggui Deng, Yang Liu, Qiguo Xu, and Gang Liu. "A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection." Symmetry 14, no. 4 (2022): 653. http://dx.doi.org/10.3390/sym14040653.

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The goal of blast-hole detection is to help place charge explosives into blast-holes. This process is full of challenges, because it requires the ability to extract sample features in complex environments, and to detect a wide variety of blast-holes. Detection techniques based on deep learning with RGB-D semantic segmentation have emerged in recent years of research and achieved good results. However, implementing semantic segmentation based on deep learning usually requires a large amount of labeled data, which creates a large burden on the production of the dataset. To address the dilemma th

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Liu, Haoyang. "The Evolution of Machine Learning in Natural Language Processing: From Traditional Methods to Deep Learning." Applied and Computational Engineering 157, no. 1 (2025): 124–31. https://doi.org/10.54254/2755-2721/2025.po24675.

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Natural language processing (NLP) has undergone a significant evolution from traditional methods to deep learning. Early rule-based parsing and statistical machine learning methods, such as hidden Markov models and conditional random fields, relied on manual feature engineering, which, although effective in tasks like text classification, had limited generalization capabilities. With the rise of deep learning, technologies represented by Recurrent Neural Network (RNNs), Transformers, and pre-trained models (Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-Trained

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Li, Wei, Junhua Gu, Benwen Chen, and Jungong Han. "Incremental Instance-Oriented 3D Semantic Mapping via RGB-D Cameras for Unknown Indoor Scene." Discrete Dynamics in Nature and Society 2020 (April 23, 2020): 1–10. http://dx.doi.org/10.1155/2020/2528954.

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Scene parsing plays a crucial role when accomplishing human-robot interaction tasks. As the “eye” of the robot, RGB-D camera is one of the most important components for collecting multiview images to construct instance-oriented 3D environment semantic maps, especially in unknown indoor scenes. Although there are plenty of studies developing accurate object-level mapping systems with different types of cameras, these methods either process the instance segmentation problem in completed mapping or suffer from a critical real-time issue due to heavy computation processing required. In this paper,

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