Academic literature on the topic 'An entity'

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Journal articles on the topic "An entity"

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Зыкова, О. А. "OFFSHORE ENTITY – A NEW BUSINESS ENTITY." Правовая мысль, no. 2(3) (January 1, 2022): 90–95. http://dx.doi.org/10.55000/mcu.legth.2022.3.2.017.

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На основании изучения доктринальных источников и законодательства, устанавливающих дефи-ницию термина «офшорная компания», автором выделены ее отличительные черты и в после-дующем дана краткая характеристика в части обоснованности, разумности их выделения и обо-значения. Так, было предложено расширение состава субъектов, которые могут входить в анали-зируемое понятие. Это было обусловлено анализом иностранных источников права (офшорных зон), который показал допустимость осуществления российскими резидентами регистрации своего предпринимательства в форме не только юридического лица, но и единол
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KIM, Eun-kyung, and Key-Sun CHOI. "Entity Summarization Based on Entity Grouping in Multilingual Projected Entity Space." IEICE Transactions on Information and Systems E100.D, no. 9 (2017): 2138–46. http://dx.doi.org/10.1587/transinf.2016edp7235.

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Qin, Yongbin, Weizhe Yang, Kai Wang, et al. "Entity Relation Extraction Based on Entity Indicators." Symmetry 13, no. 4 (2021): 539. http://dx.doi.org/10.3390/sym13040539.

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Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural
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Possemato, Tiziana. "Entity modeling." JLIS.it 13, no. 3 (2022): 12–28. http://dx.doi.org/10.36253/jlis.it-481.

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In questo lavoro tratteremo il tema delle entità intese come oggetti del mondo reale (real-world object) e di come questo concetto sia utilizzato nell’ambito dell’entity modeling, quel processo di identificazione e modellamento delle entità che tanta parte occupa nei progetti di conversione dei cataloghi in linked open data. L’aiuto alla comprensione di cosa questo concetto esprima nell’ambito dell’universo bibliografico ci viene dalla programmazione orientata agli oggetti, che introduce il concetto di modellamento e gestione di un “oggetto” definendone uno stato e un comportamento. Ma per mod
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Wang, Jiannan, Guoliang Li, Jeffrey Xu Yu, and Jianhua Feng. "Entity matching." Proceedings of the VLDB Endowment 4, no. 10 (2011): 622–33. http://dx.doi.org/10.14778/2021017.2021020.

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Getoor, Lise, and Ashwin Machanavajjhala. "Entity resolution." Proceedings of the VLDB Endowment 5, no. 12 (2012): 2018–19. http://dx.doi.org/10.14778/2367502.2367564.

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Liu, Chen, Feng Li, Xian Sun, and Hongzhe Han. "Attention-Based Joint Entity Linking with Entity Embedding." Information 10, no. 2 (2019): 46. http://dx.doi.org/10.3390/info10020046.

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Entity linking (also called entity disambiguation) aims to map the mentions in a given document to their corresponding entities in a target knowledge base. In order to build a high-quality entity linking system, efforts are made in three parts: Encoding of the entity, encoding of the mention context, and modeling the coherence among mentions. For the encoding of entity, we use long short term memory (LSTM) and a convolutional neural network (CNN) to encode the entity context and entity description, respectively. Then, we design a function to combine all the different entity information aspects
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Xu, Zhenran, Yulin Chen, and Baotian Hu. "Improving Biomedical Entity Linking with Cross-Entity Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13869–77. http://dx.doi.org/10.1609/aaai.v37i11.26624.

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Biomedical entity linking (EL) is the task of linking mentions in a biomedical document to corresponding entities in a knowledge base (KB). The challenge in biomedical EL lies in leveraging mention context to select the most appropriate entity among possible candidates. Although some EL models achieve competitive results by retrieving candidate entities and then exploiting context to re-rank them, these re-ranking models concatenate mention context with one candidate at a time. They lack fine-grained interaction among candidates, and potentially cannot handle ambiguous mentions when facing can
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Lokhande, Rohit, Namita Goyal, Sunita Bhargava, and Abha Patni. "Retroperitoneal Angiomyolipoma: Rare Entity." Asian Pacific Journal of Health Sciences 4, no. 1 (2017): 15–18. http://dx.doi.org/10.21276/apjhs.2017.4.1.5.

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S, Sujana Donandi. "TINJAUAN HUKUM MENGENAI BENTUK BADAN HUKUM LEMBAGA MANAJEMEN KOLEKTIF DI INDONESIA." Problematika Hukum 2, no. 2 (2019): 94. http://dx.doi.org/10.33021/ph.v2i2.567.

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The existing legislation does not yet give clear boundaries regarding the form of legal entity of Lembaga Manajemen Kolektif (LMK) in Indonesia. The legal certainty about the form of legal entitiy of LMK is important to be examined as the basis for government and society in running and supervising LMK. This research is conducted through a normative legal research method using statute approachment dan utilizing primary and secoundary resources. The discussion shows that LMK is divided into 2, The LMK and LMK Nasional. The form of legal entity that lawfully allowed to establish LMK is Associatio
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Dissertations / Theses on the topic "An entity"

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Samoila, Lavinia Andreea. "Multiple Entity Reconciliation." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187010.

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Living in the age of "Big Data" is both a blessing and a curse. On he one hand, the raw data can be analysed and then used for weather redictions, user recommendations, targeted advertising and more. On he other hand, when data is aggregated from multiple sources, there is no guarantee that each source has stored the data in a standardized or even compatible format to what is required by the application. So there is a need to parse the available data and convert it to the desired form. Here is where the problems start to arise: often the correspondences are not quite so straightforward between
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McKinlay, Andrew James. "Modelling entity instantiations." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/4936/.

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The problem of automatically extracting structured information from texts is an important, unsolved problem within the field of Natural Language Processing. The extraction of such information can facilitate activities such as the building of knowledge bases, automatic summarisation and sentiment analysis. A human reader can easily discern the events described in a text, along with the participants and the relationships between them, but using a computer to automatically discover the same information is much more challenging. Particular focus has been given to extracting relations between the e
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Hansen, Hugo, and Oliver Öhrström. "Benchmarking and Analysis of Entity Referencing Within Open-Source Entity Component Systems." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20074.

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Runtime performance is essential for real time games, the faster a game can run the more features designers can put into the game to accomplish their vision.A popular architecture for video games is the Entity Component System architecture aimed to improve both object composition and performance. There are many tests for how this architecture performs under its optimal linear execution.This thesis presents a performance comparison of how several popular open-source Entity Component System libraries perform when fetching data from other entities during iteration. An object-oriented test is also
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Jara-Valencia, José Luis. "Generic named entity extraction." Thesis, University of York, 2005. http://etheses.whiterose.ac.uk/14071/.

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This thesis proposes and evaluates different ways of performing generic named entity recognition, that is the construction of a system capable of recognising names in free text which is not specific to any particular domain or task. The starting point is an implementation of a well known baseline system which is based on maximum entropy models that utilise lexically-oriented features to recognised names in text. Although this system achieves good levels of performance, both maximum entropy models and lexically-oriented features have their limitations. Three alternative ways in which this syste
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Al-Olimat, Hussein S. "Knowledge-Enabled Entity Extraction." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1578100367105233.

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Benajiba, Yassine. "Arabic named entity recognition." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8318.

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En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimi
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Andersson-Säll, Tim. "Transforming Legal Entity Recognition." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447240.

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Transformer-based architectures have in recent years advanced state-of-the-art performance in Natural Language Processing. Researchers have successfully adapted such models to downstream tasks within NLP in a domain-specific setting. This thesis examines the application of these models to the legal domain by doing Named Entity Recognition (NER) in a setting of scarce training data. Three different pre-trained BERT models are fine-tuned on a set of 101 court case documents, whereof one model is pre-trained on legal corpora and the other two on general corpora. Experiments are run to evaluate th
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Horlock, James. "Named entity extraction for speech." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/29161.

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Named entity extraction, the task of finding specific entities within documents, has proven of great benefit for numerous information extraction and information retrieval tasks. As well as multiple language evaluations named entity extraction has been investigated on a variety of media forms with varying success. In general, these media forms have all been based upon standard text and assumed that any variation from standard text constitutes noise. We investigate how it is possible to find named entities in speech data. Where others have focussed on applying named entity extraction techniques
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McMillan, Marcia J. "Premenstrual depression : a distinct entity?" Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=75425.

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The validity of the Premenstrual Assessment Form (PAF) typological category "Major Depressive Syndrome", as a distinct subtype of premenstrual change, was assessed. The nature of the depression associated with the premenstrual phase was investigated with standardized measures of depression and a test of dysphoric attentional bias derived from Beck's cognitive model of depression. Three subject groups were delineated based on prospective daily ratings: PMD (depression only premenstrually), controls (no depression), and INTD (intermittent depression throughout cycle). The control group did not e
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MENEZES, DANIEL SPECHT SILVA. "NAMED ENTITY RECOGNITION FOR PORTUGUESE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35855@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>BOLSA NOTA 10<br>A produção e acesso a quantidades imensas dados é um elemento pervasivo da era da informação. O volume de informação disponível é sem precedentes na história da humanidade e está sobre constante processo de expansão. Uma oportunidade que emerge neste ambiente é o desenvolvimento de aplicações que sejam capazes de estruturar conhecimento contido nesses dados. Neste co
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Books on the topic "An entity"

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Leever, Patricia. Entity. Omnific Publishing, 2014.

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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3.

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Patel, M. F. Indian village, from entity to non-entity. Kanak Publications, 1990.

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Gorman, Brian L. Practical Entity Framework. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6044-9.

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Thalheim, Bernhard. Entity-Relationship Modeling. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04058-4.

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Balog, Krisztian. Entity-Oriented Search. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93935-3.

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Yan, Yao. Entity field physics. Data-Infor Consultants & Services, 1994.

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Streng, William P. Choice of entity. Tax Management, 1999.

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Balog, Krisztian. Entity-Oriented Search. Springer Nature, 2018.

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Richard, Barker. Entity relationship modelling. Addison-Wesley, 1990.

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Book chapters on the topic "An entity"

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Weik, Martin H. "entity." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_6278.

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Cropanzano, Russell, Erica L. Anthony, Shanna R. Daniels, and Alison V. Hall. "Entity Justice and Entity Injustice." In Organizational Justice. Routledge, 2017. http://dx.doi.org/10.4324/9781315648194-10.

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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Recent Advance of Representation Learning Stage." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_3.

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AbstractOver the last few years, there are a pile of research devoted to learning better KG representations to facilitate entity alignment. Thus, in this chapter, we summarize recent progress in the representation learning stage of EA and also provide a detailed empirical evaluation to reveal the strengths and weaknesses of current solutions.
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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Weakly Supervised Entity Alignment." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_7.

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AbstractThe majority of state-of-the-art entity alignment solutions heavily rely on the labeled data, which are difficult to obtain in practice. Therefore, it calls for the study of EA with scarce supervision. To resolve this issue, we put forward a reinforced active entity alignment framework to select the entities to be manually labeled with the aim of enhancing alignment performance with minimal labeling efforts. Under this framework, we further devise an unsupervised contrastive loss to contrast different views of entity representations and augment the limited supervision signals by exploi
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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Long-Tail Entity Alignment." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_6.

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AbstractMost entity alignment solutions currently rely on structural information, specifically KG embedding, to align entities. However, in real-life KGs, the majority of entities have a sparse neighborhood structure, while only a few entities are densely connected to others. These less-connected entities are referred to as long-tail entities, and this phenomenon limits the effectiveness of using structural information for entity alignment.To address this issue, we propose an approach that incorporates entity name information, which is often overlooked but readily available. We amplify the wea
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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Recent Advance of Alignment Inference Stage." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_4.

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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "State-of-the-Art Approaches." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_2.

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AbstractThis chapter performs a thorough assessment and meticulous examination of the most advanced EA techniques. Initially, we introduce a broad EA framework that covers all current methods and classify these methods into three main groups. Then, we carefully appraise these solutions on various scenarios, taking into account their efficacy, efficiency, and scalability. Lastly, we create a novel EA dataset that reflects the actual difficulties encountered in alignment, which prior literature mostly ignored. This chapter aims to offer a comprehensive understanding of the advantages and drawbac
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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Multimodal Entity Alignment." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_9.

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AbstractIn various tasks related to artificial intelligence, data is often present in multiple forms or modalities. Recently, it has become a popular approach to combine these different forms of information into a knowledge graph, creating a multi-modal knowledge graph (MMKG). However, multi-modal knowledge graphs (MMKGs) often face issues of insufficient data coverage and incompleteness. In order to address this issue, a possible strategy is to incorporate supplemental information from other multi-modal knowledge graphs (MMKGs). To achieve this goal, current methods for aligning entities coul
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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Unsupervised Entity Alignment." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_8.

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AbstractState-of-the-art entity alignment solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled da
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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Introduction to Entity Alignment." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_1.

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Conference papers on the topic "An entity"

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Shavarani, Hassan, and Anoop Sarkar. "Entity Retrieval for Answering Entity-Centric Questions." In Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.knowledgenlp-1.1.

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Ding, Guohui, Wenjing Tang, and Zhaoyi Yuan. "Entity Label-Guided Graph Fusion Multi-Modal Named Entity Recognition." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831708.

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Wen, Haoyang, Eduard Hovy, and Alexander Hauptmann. "Transitive Consistency Constrained Learning for Entity-to-Entity Stance Detection." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.80.

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Yang, Haihong, Ming Qin, Qiang Zhang, and Renjun Xu. "Language-agnostic Zero-shot Entity Linking via Entity Prototype Network." In 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00062.

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Zhang, Guobiao, Xueping Peng, Tao Shen, et al. "Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.810.

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Procopio, Luigi, Simone Conia, Edoardo Barba, and Roberto Navigli. "Entity Disambiguation with Entity Definitions." In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.eacl-main.93.

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Gerritse, Emma J., Faegheh Hasibi, and Arjen P. de Vries. "Entity-aware Transformers for Entity Search." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022. http://dx.doi.org/10.1145/3477495.3531971.

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Lu, Chunliang, Wai Lam, and Yi Liao. "Entity Retrieval via Entity Factoid Hierarchy." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-1050.

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Wang, Junxiong, Ali Mousavi, Omar Attia, et al. "Entity Disambiguation via Fusion Entity Decoding." In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.naacl-long.363.

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Balasubramanyan, Ramnath, and William W. Cohen. "Block-LDA: Jointly modeling entity-annotated text and entity-entity links." In Proceedings of the 2011 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611972818.39.

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Reports on the topic "An entity"

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Chisholm, S., and D. Perkins. Entity State MIB. RFC Editor, 2005. http://dx.doi.org/10.17487/rfc4268.

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McCloghrie, K., and A. Bierman. Entity MIB using SMIv2. RFC Editor, 1996. http://dx.doi.org/10.17487/rfc2037.

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McCloghrie, K., and A. Bierman. Entity MIB (Version 2). RFC Editor, 1999. http://dx.doi.org/10.17487/rfc2737.

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Bierman, A., and K. McCloghrie. Entity MIB (Version 3). RFC Editor, 2005. http://dx.doi.org/10.17487/rfc4133.

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Bierman, A., D. Romascanu, J. Quittek, and M. Chandramouli. Entity MIB (Version 4). RFC Editor, 2013. http://dx.doi.org/10.17487/rfc6933.

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Senehi, M. K., Sarah Wallace, Ed Barkmeyer, Steven Ray, and Evan K. Wallace. Control entity interface document. National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4626.

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Barkmeyer, Ed, Steven Ray, and Evan K. Wallace. Control entity interface specification. National Institute of Standards and Technology, 1993. http://dx.doi.org/10.6028/nist.ir.5272.

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Bierman, A., D. Romascanu, and K. C. Norseth. Entity Sensor Management Information Base. RFC Editor, 2002. http://dx.doi.org/10.17487/rfc3433.

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Lundblade, L., G. Mandyam, J. O'Donoghue, and C. Wallace. The Entity Attestation Token (EAT). RFC Editor, 2025. https://doi.org/10.17487/rfc9711.

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Wakid, Shukri. Entity Authentication Using Public Key Cryptography. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada408513.

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