Literatura científica selecionada sobre o tema "Semantic concepts extraction"
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Artigos de revistas sobre o assunto "Semantic concepts extraction"
Huang, Jingxiu, Ruofei Ding, Xiaomin Wu, Shumin Chen, Jiale Zhang, Lixiang Liu e Yunxiang Zheng. "WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement". Applied Sciences 13, n.º 22 (14 de novembro de 2023): 12307. http://dx.doi.org/10.3390/app132212307.
Texto completo da fonteLi, Dao Wang. "Research on Text Conceptual Relation Extraction Based on Domain Ontology". Advanced Materials Research 739 (agosto de 2013): 574–79. http://dx.doi.org/10.4028/www.scientific.net/amr.739.574.
Texto completo da fonteKatsadaki, Eirini, e Margarita Kokla. "Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment". AGILE: GIScience Series 5 (30 de maio de 2024): 1–7. http://dx.doi.org/10.5194/agile-giss-5-32-2024.
Texto completo da fonteAlArfaj, Abeer. "Towards relation extraction from Arabic text: a review". International Robotics & Automation Journal 5, n.º 5 (24 de dezembro de 2019): 212–15. http://dx.doi.org/10.15406/iratj.2019.05.00195.
Texto completo da fonteJi, Lei, Yujing Wang, Botian Shi, Dawei Zhang, Zhongyuan Wang e Jun Yan. "Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding". Data Intelligence 1, n.º 3 (junho de 2019): 238–70. http://dx.doi.org/10.1162/dint_a_00013.
Texto completo da fontePapadias, Evangelos, Margarita Kokla e Eleni Tomai. "Educing knowledge from text: semantic information extraction of spatial concepts and places". AGILE: GIScience Series 2 (4 de junho de 2021): 1–7. http://dx.doi.org/10.5194/agile-giss-2-38-2021.
Texto completo da fonteHong Doan, Phuoc Thi, Ngamnij Arch-int e Somjit Arch-int. "A Semantic Framework for Extracting Taxonomic Relations from Text Corpus". International Arab Journal of Information Technology 17, n.º 3 (1 de maio de 2019): 325–37. http://dx.doi.org/10.34028/iajit/17/3/6.
Texto completo da fonteChahal, Poonam, Manjeet Singh e Suresh Kumar. "Semantic Analysis Based Approach for Relevant Text Extraction Using Ontology". International Journal of Information Retrieval Research 7, n.º 4 (outubro de 2017): 19–36. http://dx.doi.org/10.4018/ijirr.2017100102.
Texto completo da fonteAbbas, Asim, Muhammad Afzal, Jamil Hussain, Taqdir Ali, Hafiz Syed Muhammad Bilal, Sungyoung Lee e Seokhee Jeon. "Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation". International Journal of Environmental Research and Public Health 18, n.º 20 (9 de outubro de 2021): 10564. http://dx.doi.org/10.3390/ijerph182010564.
Texto completo da fonteArnold, Patrick, e Erhard Rahm. "Automatic Extraction of Semantic Relations from Wikipedia". International Journal on Artificial Intelligence Tools 24, n.º 02 (abril de 2015): 1540010. http://dx.doi.org/10.1142/s0218213015400102.
Texto completo da fonteTeses / dissertações sobre o assunto "Semantic concepts extraction"
Liang, Antoni. "Face Image Retrieval with Landmark Detection and Semantic Concepts Extraction". Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/54081.
Texto completo da fonteTang, My Thao. "Un système interactif et itératif extraction de connaissances exploitant l'analyse formelle de concepts". Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0060/document.
Texto completo da fonteIn this thesis, we present a methodology for interactive and iterative extracting knowledge from texts - the KESAM system: A tool for Knowledge Extraction and Semantic Annotation Management. KESAM is based on Formal Concept Analysis for extracting knowledge from textual resources that supports expert interaction. In the KESAM system, knowledge extraction and semantic annotation are unified into one single process to benefit both knowledge extraction and semantic annotation. Semantic annotations are used for formalizing the source of knowledge in texts and keeping the traceability between the knowledge model and the source of knowledge. The knowledge model is, in return, used for improving semantic annotations. The KESAM process has been designed to permanently preserve the link between the resources (texts and semantic annotations) and the knowledge model. The core of the process is Formal Concept Analysis that builds the knowledge model, i.e. the concept lattice, and ensures the link between the knowledge model and annotations. In order to get the resulting lattice as close as possible to domain experts' requirements, we introduce an iterative process that enables expert interaction on the lattice. Experts are invited to evaluate and refine the lattice; they can make changes in the lattice until they reach an agreement between the model and their own knowledge or application's need. Thanks to the link between the knowledge model and semantic annotations, the knowledge model and semantic annotations can co-evolve in order to improve their quality with respect to domain experts' requirements. Moreover, by using FCA to build concepts with definitions of sets of objects and sets of attributes, the KESAM system is able to take into account both atomic and defined concepts, i.e. concepts that are defined by a set of attributes. In order to bridge the possible gap between the representation model based on a concept lattice and the representation model of a domain expert, we then introduce a formal method for integrating expert knowledge into concept lattices in such a way that we can maintain the lattice structure. The expert knowledge is encoded as a set of attribute dependencies which is aligned with the set of implications provided by the concept lattice, leading to modifications in the original lattice. The method also allows the experts to keep a trace of changes occurring in the original lattice and the final constrained version, and to access how concepts in practice are related to concepts automatically issued from data. The method uses extensional projections to build the constrained lattices without changing the original data and provide the trace of changes. From an original lattice, two different projections produce two different constrained lattices, and thus, the gap between the representation model based on a concept lattice and the representation model of a domain expert is filled with projections
Tang, My Thao. "Un système interactif et itératif extraction de connaissances exploitant l'analyse formelle de concepts". Electronic Thesis or Diss., Université de Lorraine, 2016. http://www.theses.fr/2016LORR0060.
Texto completo da fonteIn this thesis, we present a methodology for interactive and iterative extracting knowledge from texts - the KESAM system: A tool for Knowledge Extraction and Semantic Annotation Management. KESAM is based on Formal Concept Analysis for extracting knowledge from textual resources that supports expert interaction. In the KESAM system, knowledge extraction and semantic annotation are unified into one single process to benefit both knowledge extraction and semantic annotation. Semantic annotations are used for formalizing the source of knowledge in texts and keeping the traceability between the knowledge model and the source of knowledge. The knowledge model is, in return, used for improving semantic annotations. The KESAM process has been designed to permanently preserve the link between the resources (texts and semantic annotations) and the knowledge model. The core of the process is Formal Concept Analysis that builds the knowledge model, i.e. the concept lattice, and ensures the link between the knowledge model and annotations. In order to get the resulting lattice as close as possible to domain experts' requirements, we introduce an iterative process that enables expert interaction on the lattice. Experts are invited to evaluate and refine the lattice; they can make changes in the lattice until they reach an agreement between the model and their own knowledge or application's need. Thanks to the link between the knowledge model and semantic annotations, the knowledge model and semantic annotations can co-evolve in order to improve their quality with respect to domain experts' requirements. Moreover, by using FCA to build concepts with definitions of sets of objects and sets of attributes, the KESAM system is able to take into account both atomic and defined concepts, i.e. concepts that are defined by a set of attributes. In order to bridge the possible gap between the representation model based on a concept lattice and the representation model of a domain expert, we then introduce a formal method for integrating expert knowledge into concept lattices in such a way that we can maintain the lattice structure. The expert knowledge is encoded as a set of attribute dependencies which is aligned with the set of implications provided by the concept lattice, leading to modifications in the original lattice. The method also allows the experts to keep a trace of changes occurring in the original lattice and the final constrained version, and to access how concepts in practice are related to concepts automatically issued from data. The method uses extensional projections to build the constrained lattices without changing the original data and provide the trace of changes. From an original lattice, two different projections produce two different constrained lattices, and thus, the gap between the representation model based on a concept lattice and the representation model of a domain expert is filled with projections
Joseph, Daniel. "Linking information resources with automatic semantic extraction". Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/linking-information-resources-with-automatic-semantic-extraction(ada2db36-4366-441a-a0a9-d76324a77e2c).html.
Texto completo da fonteDe, Maio Carmen. "Fuzzy concept analysis for semantic knowledge extraction". Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/1307.
Texto completo da fonteAvailability of controlled vocabularies, ontologies, and so on is enabling feature to provide some added values in terms of knowledge management. Nevertheless, the design, maintenance and construction of domain ontologies are a human intensive and time consuming task. The Knowledge Extraction consists of automatic techniques aimed to identify and to define relevant concepts and relations of the domain of interest by analyzing structured (relational databases, XML) and unstructured (text, documents, images) sources. Specifically, methodology for knowledge extraction defined in this research work is aimed at enabling automatic ontology/taxonomy construction from existing resources in order to obtain useful information. For instance, the experimental results take into account data produced with Web 2.0 tools (e.g., RSS-Feed, Enterprise Wiki, Corporate Blog, etc.), text documents, and so on. Final results of Knowledge Extraction methodology are taxonomies or ontologies represented in a machine oriented manner by means of semantic web technologies, such as: RDFS, OWL and SKOS. The resulting knowledge models have been applied to different goals. On the one hand, the methodology has been applied in order to extract ontologies and taxonomies and to semantically annotate text. On the other hand, the resulting ontologies and taxonomies are exploited in order to enhance information retrieval performance and to categorize incoming data and to provide an easy way to find interesting resources (such as faceted browsing). Specifically, following objectives have been addressed in this research work: Ontology/Taxonomy Extraction: that concerns to automatic extraction of hierarchical conceptualizations (i.e., taxonomies) and relations expressed by means typical description logic constructs (i.e., ontologies). Information Retrieval: definition of a technique to perform concept-based the retrieval of information according to the user queries. Faceted Browsing: in order to automatically provide faceted browsing capabilities according to the categorization of the extracted contents. Semantic Annotation: definition of a text analysis process, aimed to automatically annotate subjects and predicates identified. The experimental results have been obtained in some application domains: e-learning, enterprise human resource management, clinical decision support system. Future challenges go in the following directions: investigate approaches to support ontology alignment and merging applied to knowledge management.
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Caubriere, Antoine. "Du signal au concept : réseaux de neurones profonds appliqués à la compréhension de la parole". Thesis, Le Mans, 2021. https://tel.archives-ouvertes.fr/tel-03177996.
Texto completo da fonteThis thesis is part of the deep learning applied to spoken language understanding. Until now, this task was performed through a pipeline of components implementing, for example, a speech recognition system, then different natural language processing, before involving a language understanding system on enriched automatic transcriptions. Recently, work in the field of speech recognition has shown that it is possible to produce a sequence of words directly from the acoustic signal. Within the framework of this thesis, the aim is to exploit these advances and extend them to design a system composed of a single neural model fully optimized for the spoken language understanding task, from signal to concept. First, we present a state of the art describing the principles of deep learning, speech recognition, and speech understanding. Then, we describe the contributions made along three main axes. We propose a first system answering the problematic posed and apply it to a task of named entities recognition. Then, we propose a transfer learning strategy guided by a curriculum learning approach. This strategy is based on the generic knowledge learned to improve the performance of a neural system on a semantic concept extraction task. Then, we perform an analysis of the errors produced by our approach, while studying the functioning of the proposed neural architecture. Finally, we set up a confidence measure to evaluate the reliability of a hypothesis produced by our system
Kulkarni, Swarnim. "Capturing semantics using a link analysis based concept extractor approach". Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1526.
Texto completo da fonteMendes, Pablo N. "Adaptive Semantic Annotation of Entity and Concept Mentions in Text". Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504.
Texto completo da fonteTolle, Kristin M. "Domain-independent semantic concept extraction using corpus linguistics, statistics and artificial intelligence techniques". Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280502.
Texto completo da fontePelloin, Valentin. "La compréhension de la parole dans les systèmes de dialogues humain-machine à l'heure des modèles pré-entraînés". Electronic Thesis or Diss., Le Mans, 2024. http://www.theses.fr/2024LEMA1002.
Texto completo da fonteIn this thesis, spoken language understanding (SLU) is studied in the application context of telephone dialogues with defined goals (hotel booking reservations, for example). Historically, SLU was performed through a cascade of systems: a first system would transcribe the speech into words, and a natural language understanding system would link those words to a semantic annotation. The development of deep neural methods has led to the emergence of end-to-end architectures, where the understanding task is performed by a single system, applied directly to the speech signal to extract the semantic annotation. Recently, so-called self-supervised learning (SSL) pre-trained models have brought new advances in natural language processing (NLP). Learned in a generic way on very large datasets, they can then be adapted for other applications. To date, the best SLU results have been obtained with pipeline systems incorporating SSL models.However, none of the architectures, pipeline or end-to-end, is perfect. In this thesis, we study these architectures and propose hybrid versions that attempt to benefit from the advantages of each. After developing a state-of-the-art end-to-end SLU model, we evaluated different hybrid strategies. The advances made by SSL models during the course of this thesis led us to integrate them into our hybrid architecture
Livros sobre o assunto "Semantic concepts extraction"
Yang, Sijia, e Sandra González-Bailón. Semantic Networks and Applications in Public Opinion Research. Editado por Jennifer Nicoll Victor, Alexander H. Montgomery e Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.14.
Texto completo da fonteCapítulos de livros sobre o assunto "Semantic concepts extraction"
Atapattu, Thushari, Katrina Falkner e Nickolas Falkner. "Automated Extraction of Semantic Concepts from Semi-structured Data: Supporting Computer-Based Education through the Analysis of Lecture Notes". In Lecture Notes in Computer Science, 161–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32600-4_13.
Texto completo da fonteCavaliere, Danilo, e Sabrina Senatore. "Emotional Concept Extraction Through Ontology-Enhanced Classification". In Metadata and Semantic Research, 52–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36599-8_5.
Texto completo da fonteTosi, Mauro Dalle Lucca, e Julio Cesar dos Reis. "C-Rank: A Concept Linking Approach to Unsupervised Keyphrase Extraction". In Metadata and Semantic Research, 236–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36599-8_21.
Texto completo da fonteYuan, Siyu, Deqing Yang, Jiaqing Liang, Jilun Sun, Jingyue Huang, Kaiyan Cao, Yanghua Xiao e Rui Xie. "Large-Scale Multi-granular Concept Extraction Based on Machine Reading Comprehension". In The Semantic Web – ISWC 2021, 93–110. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88361-4_6.
Texto completo da fontePark, Kyung-Wook, e Dong-Ho Lee. "Full-Automatic High-Level Concept Extraction from Images Using Ontologies and Semantic Inference Rules". In The Semantic Web – ASWC 2006, 307–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11836025_31.
Texto completo da fonteGhannay, Sahar, Antoine Caubrière, Salima Mdhaffar, Gaëlle Laperrière, Bassam Jabaian e Yannick Estève. "Where Are We in Semantic Concept Extraction for Spoken Language Understanding?" In Speech and Computer, 202–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87802-3_19.
Texto completo da fonteAL-Aswadi, Fatima N., Huah Yong Chan e Keng Hoon Gan. "Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning". In Lecture Notes on Data Engineering and Communications Technologies, 374–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70713-2_35.
Texto completo da fonteLi, Yanlin, e Chu-Ren Huang. "Extracting Concepts and Semantic Associates for Teaching Tang 300 Poems to L2 Learners". In Lecture Notes in Computer Science, 233–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28953-8_18.
Texto completo da fonteZentgraf, Sven, Sherief Ali e Markus König. "Concept for Enriching NISO-STS Standards with Machine-Readable Requirements and Validation Rules". In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 718–28. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.72.
Texto completo da fonteZentgraf, Sven, Sherief Ali e Markus König. "Concept for Enriching NISO-STS Standards with Machine-Readable Requirements and Validation Rules". In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 718–28. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.72.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Semantic concepts extraction"
Hübner, Marc, Christoph Alt, Robert Schwarzenberg e Leonhard Hennig. "Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction". In Proceedings of the Fourteenth Workshop on Semantic Evaluation. Stroudsburg, PA, USA: International Committee for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.semeval-1.92.
Texto completo da fonteTian, Qingwen, Shixing Zhou, Yu Cheng, Jianxia Chen, Yi Gao e Shuijing Zhang. "Curriculum Semantic Retrieval System based on Distant Supervision". In 7th International Conference on Software Engineering and Applications (SOFEA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111603.
Texto completo da fonteChen, Jiaoyan, Freddy Lecue, Jeff Z. Pan e Huajun Chen. "Learning from Ontology Streams with Semantic Concept Drift". 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.2017/133.
Texto completo da fonteTakeda, Hideaki, Susumu Hamada, Tetsuo Tomiyama e Hiroyuki Yoshikawa. "A Cognitive Approach to the Analysis of Design Processes". In ASME 1990 Design Technical Conferences. American Society of Mechanical Engineers, 1990. http://dx.doi.org/10.1115/detc1990-0121.
Texto completo da fonteShi, Botian, Lei Ji, Pan Lu, Zhendong Niu e Nan Duan. "Knowledge Aware Semantic Concept Expansion for Image-Text Matching". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/720.
Texto completo da fontePopa, Ramona cristina, Nicolae Goga e Bujor ionel Pavaloiu. "PROVIDING SEMANTICALLY-ENABLED INFORMATION FOR SMES KNOWLEDGE WORKERS: MULTI-AGENT-BASED MIDDLEWARE". In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-046.
Texto completo da fonteTonelli, Sara, Marco Rospocher, Emanuele Pianta e Luciano Serafini. "Boosting Collaborative Ontology Building with Key-Concept Extraction". In 2011 IEEE Fifth International Conference on Semantic Computing (ICSC). IEEE, 2011. http://dx.doi.org/10.1109/icsc.2011.21.
Texto completo da fonteKang, SungKu, Lalit Patil, Arvind Rangarajan, Abha Moitra, Tao Jia, Dean Robinson e Debasish Dutta. "Extraction of Manufacturing Rules From Unstructured Text Using a Semantic Framework". In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47556.
Texto completo da fonteArnold, Patrick, e Erhard Rahm. "Extracting Semantic Concept Relations from Wikipedia". In the 4th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2611040.2611079.
Texto completo da fonteWei, Xiao, e Xiangfeng Luo. "Concept Extraction based on Association Linked Network". In 2010 Sixth International Conference on Semantics Knowledge and Grid (SKG). IEEE, 2010. http://dx.doi.org/10.1109/skg.2010.11.
Texto completo da fonte