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Journal articles on the topic 'Hybrid entity'

Author: Grafiati

Published: 13 April 2024

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1

Parada, Leopoldo. "Hybrid Entity Mismatches and the International Trend of Matching Tax Outcomes: A Critical Approach." Intertax 46, Issue 12 (December 1, 2018): 971–93. http://dx.doi.org/10.54648/taxi2018104.

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The international tax debate as regards hybrid entities has certainly changed after the OECD BEPS Project. Since then, the trend has been focused exclusively on matching transactions involving hybrids and reverse hybrid entities and double non-taxation, however without questioning whether these two elements are necessarily interconnected or whether they should serve each other in the design of domestic anti-hybrid provisions. This is particularly evident as regards the notion of ‘hybrid (entity) mismatch arrangements’ the design of which is based on the assumption that income should be taxed somewhere – no matter where – and as regards the complex set of domestic solutions proposed (‘linking rules’) the true efficacy of which is nevertheless still unknown. In this vein, this article adopts a critical approach both as regards the diagnosis of the problems and as regards the solutions proposed. As to the diagnosis, the author argues that the artificial attempt to match transactions involving hybrid entities and double non-taxation not only disregards the fundamental issue as regards hybrid entity mismatches (i.e. the disparate tax characterization of the same entity by two different states), but also carries with it the risk of creating presumptions of abusive practices in all those cases in which a hybrid entity structure is simply not taxed at all. As to the remedies, the author argues against the complexity, excessive reliance on foreign laws and potential economic double taxation issues that the implementation of linking rules might cause from a tax policy perspective. From a practical perspective, the author questions the proper interaction between linking rules and other anti-base erosion provisions, such as interest limitations and CFC rules. This article ultimately concludes that a re-orientation in the international debate regarding hybrids and reverse hybrid entities is crucial in order to open the door for more fundamental – and perhaps also more coordinated – solutions.

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2

Chaturvedi, Nikhil, and Jigyasu Dubey. "Hybrid Model for Named Entity Recognition." International Journal of Distributed Artificial Intelligence 14, no. 1 (January 1, 2022): 1–12. http://dx.doi.org/10.4018/ijdai.311063.

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Named entity recognition is an important factor that has a direct and significant impact on the quality of neural sequence labelling. It entails choosing encoding input data to create grammatical and semantic representation vectors. The main goal of this research is to provide a hybrid neural network model for a specific sequence labelling task such as named entity recognition. Three subnetworks are used in this hybrid model to ensure that information at the character, capitalization levels, and word-level contextual representation is fully utilized. The authors used different samples for training and development sets on the CoNLL-2003 dataset to show that the model could compare its performance to that of other state-of-the-art models.

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SinghBajwa, Kanwalpreet, and Amardeep Kaur. "Hybrid Approach for Named Entity Recognition." International Journal of Computer Applications 118, no. 1 (May 20, 2015): 36–41. http://dx.doi.org/10.5120/20713-3048.

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Nguyen, Hien T., and Tru H. Cao. "NAMED ENTITY DISAMBIGUATION: A HYBRID APPROACH." International Journal of Computational Intelligence Systems 5, no. 6 (November 2012): 1052–67. http://dx.doi.org/10.1080/18756891.2012.747661.

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Singh, Ravinder, Monika Gupta, Shailja Chatterjee, and Karandeep Singh Arora. "Hybrid ameloblastoma: a mystified histopathological entity." BMJ Case Reports 12, no. 4 (April 2019): e229834. http://dx.doi.org/10.1136/bcr-2019-229834.

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Ameloblastoma a benign neoplasm of the maxillofacial region has been divided into various histopathological types by WHO. A more complex and confusing type includes hybrid type, which as the name suggest include more than two variants mostly histopathological. Various authors have reported cases of this type but the exact histopathological features are still unclear and each case that is being reported add to the literature, which further strengthens its histopathological feature. Also, this is a lesion whose clinical and radiographical features are similar to all the variants and a definitive diagnosis is achieved by histopathology only. Here, we present a case of hybrid ameloblastoma with striking and unique histopathological features.

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Lee, Jongwuk, Hyunsouk Cho, Jin-Woo Park, Young-rok Cha, Seung-won Hwang, Zaiqing Nie, and Ji-Rong Wen. "Hybrid entity clustering using crowds and data." VLDB Journal 22, no. 5 (August 13, 2013): 711–26. http://dx.doi.org/10.1007/s00778-013-0328-8.

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Küçük, Dilek, and Adnan Yazıcı. "A hybrid named entity recognizer for Turkish." Expert Systems with Applications 39, no. 3 (February 2012): 2733–42. http://dx.doi.org/10.1016/j.eswa.2011.08.131.

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8

Parada, Leopoldo. "Hybrid Entity Mismatches and the MLI: A Tax Policy Assessment." Intertax 49, Issue 10 (October 1, 2021): 786–802. http://dx.doi.org/10.54648/taxi2021085.

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This article argues that despite its apparent success as a political instrument to achieve global coordination, and particularly referred to hybrid entity mismatches, the multilateral instrument (MLI) has failed. Most notably, the MLI has been incapable of keeping cohesion with the main object and purpose of tax treaties, reinforcing also an unequal distribution of taxing powers between residence and source states. In light of the above, this article explores some prospective alternatives that could not only help with restoring cohesion and equality within tax treaties, but also add certainty and simplicity to the interpretation of the MLI and the issues related to hybrid entity mismatches. Hybrid entity mismatches, hybrid entities, transparent entities, MLI, BEPS, multilateral instruments, coordination of entities, saving clause, beneficial owner, entity classification.

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Blanco-Oliver, A., A. Irimia-Dieguez, M. D. Oliver-Alfonso, and M. J. Vázquez-Cueto. "Hybrid model using logit and nonparametric methods for predicting micro-entity failure." Investment Management and Financial Innovations 13, no. 3 (August 23, 2016): 35–46. http://dx.doi.org/10.21511/imfi.13(3).2016.03.

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Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

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10

Parada, Leopoldo. "Hybrid Entity Mismatches: Exploring Three Alternatives for Coordination." Intertax 47, Issue 1 (January 1, 2019): 24–54. http://dx.doi.org/10.54648/taxi2019003.

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The OECD pragmatic approach regarding hybrid entity mismatches is, without doubt, questionable. However, equally questionable is the absence of alternatives solutions proposed by either academics or tax policy makers , which demonstrates a sort of conformism as regards both the diagnosis of the problems and the solutions thereto, as if matching tax outcomes and taxing income somewhere – no matter where – were indeed the only possible path to deal with hybrid entity mismatches. In an attempt to break this inertia, this article argues for coordination in the tax characterization of entities as a straightforward and suitable alternative to replace the current OECD linking rules, and perhaps also, the consequentialist OECD approach to hybrid entity mismatches. For this purpose, three specific alternatives are explored for coordination in the tax characterization of entities, which include (1) supremacy of the tax characterization rules of the source state, (2) supremacy of the tax characterization rules of the residence state and (3) supremacy of the tax characterization rules of the home state. The analysis of these alternatives includes both hypotheticals and specific examples from domestic and supranational laws that are used to illustrate and support their effectiveness. The ultimate aim of this article is to demonstrate that coordination in the tax characterization of entities appears to be not only a more preferable path when compared to the OECD approach of matching tax outcomes, but also a more coherent and less costly alternative for both taxpayers and tax administrations.

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Ahmed, Mohamed Ben, and Imane Allaouzi. "French medical named entity recognition: a hybrid approach." International Journal of Intelligent Enterprise 6, no. 2/3/4 (2019): 341. http://dx.doi.org/10.1504/ijie.2019.10022699.

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Allaouzi, Imane, and Mohamed Ben Ahmed. "French medical named entity recognition: a hybrid approach." International Journal of Intelligent Enterprise 6, no. 2/3/4 (2019): 341. http://dx.doi.org/10.1504/ijie.2019.101136.

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Moradi, Hamed, Farid Ahmadi, and Mohammad-Reza Feizi-Derakhshi. "A Hybrid Approach for Persian Named Entity Recognition." Iranian Journal of Science and Technology, Transactions A: Science 41, no. 1 (March 2017): 215–22. http://dx.doi.org/10.1007/s40995-017-0209-x.

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Shaalan, Khaled, and Mai Oudah. "A hybrid approach to Arabic named entity recognition." Journal of Information Science 40, no. 1 (October 16, 2013): 67–87. http://dx.doi.org/10.1177/0165551513502417.

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15

Virág, Ágnes. "The Parliament about the Clouds: Motivational Factors of Metaforic Hybrids." JEL-KÉP, no. 2 (2022): 1–24. http://dx.doi.org/10.20520/jel-kep.2022.2.1.

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One of the most known visual metaphors is hybrid (Forceville 2002), a conceptual process in which the visual target and the visual source domains are integrated into one entity providing a high potential of incongruency (e.g., a cat with elephant ears). Previous research mostly focused on hybrids in isolation and studied their physical features as potential motivational factors behind them. The present paper aims to systematically investigate the hybrids with their motivational factors in discourse and provides a solution to the conceptual collaborations including a metaphoric hybrid. As a main result of the study, analogous priming is hypothesized as a potential motivating factor in hybrids meaning that more conceptual metaphors are construed at the same time out of which at least one is a metaphoric hybrid. It can appear in two ways: (1) when the metaphoric hybrid is intertwined with another conceptual metaphor different from the hybrid, or (2) when the target domain (being part of the hybrid) is shown by two or more sources.

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16

Pratama, Rivanda Putra, Rahmat Hidayat, Nisrina Fadhilah Fano, Adam Akbar, and Nur Aini Rakhmawati. "Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku)." JISKA (Jurnal Informatika Sunan Kalijaga) 6, no. 3 (September 22, 2021): 130–38. http://dx.doi.org/10.14421/jiska.2021.6.3.130-138.

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Data processing speed in companies is important to speed up their analysis. Entity matching is a computational process that companies can perform in data processing. In conducting data processing, entity matching plays a role in determining two different data but referring to the same entity. Entity matching problems arise when the dataset used in the comparison is large. The deep learning concept is one of the solutions in dealing with entity matching problems. DeepMatcher is a python package based on a deep learning model architecture that can solve entity matching problems. The purpose of this study was to determine the matching between the two datasets with the application of DeepMatcher in entity matching using drug data from farmaku.com and k24klik.com. The comparison model used is the Hybrid model. Based on the test results, the Hybrid model produces accurate numbers, so that the entity matching used in this study runs well. The best accuracy value of the 10th training with an F1 value of 30.30, a precision value of 17.86, and a recall value of 100.

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17

Stevens, Ton, and G. K. Fibbe. "Taxation of Hybrid Entities under the Parent-Subsidiary Directive: The Example of the Netherlands." EC Tax Review 20, Issue 5 (October 1, 2011): 242–54. http://dx.doi.org/10.54648/ecta2011028.

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In this article, the application of the Parent-Subsidiary Directive in respect of hybrid entities is analysed by Prof. Ton Stevens and Dr Gijs Fibbe. A distinction is made between outbound and inbound situations. In outbound situations, Member States can differ of opinion on which is the profit-generating entity: the parent company or its hybrid subsidiary. In inbound situations, where a dividend is paid to a hybrid entity, Member States can differ of opinion about the question to whom the dividend is paid: the hybrid entity or its participants. The authors describe the explicit solution in outbound situations included in Article 4, paragraph 1a, of the Parent-Subsidiary Directive. For inbound situations, such explicit provision addressing hybrid entities is missing. The authors describe how according to them the Parent-Subsidiary Directive should be applied in respect of hybrid entities in inbound situations. After a thorough analysis, they describe why in their view Member States are, for purposes of the Parent-Subsidiary Directive, no longer autonomous in the tax classification and income allocation in respect of hybrid entities established in other Member States in inbound situations.

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18

Xiao, Ya, Chengxiang Tan, Zhijie Fan, Qian Xu, and Wenye Zhu. "Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9314–21. http://dx.doi.org/10.1609/aaai.v34i05.6471.

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Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences.

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Coutinho, José, Gonçalo Coutinho, Cecília Caldas, and Francisco Salvado. "Oral hybrid verrucous carcinoma: A challenging entity to clinicians." European Journal of Surgical Oncology 47, no. 2 (February 2021): e56. http://dx.doi.org/10.1016/j.ejso.2020.11.292.

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HINCHY, N., M. SULLIVAN, and M. MAHDIAN. "MANDIBULAR HYBRID CGCG-COF: REPORT OF A RARE ENTITY." Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 132, no. 3 (September 2021): e119-e120. http://dx.doi.org/10.1016/j.oooo.2021.04.039.

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21

Rocktäschel, Tim, Michael Weidlich, and Ulf Leser. "ChemSpot: a hybrid system for chemical named entity recognition." Bioinformatics 28, no. 12 (April 12, 2012): 1633–40. http://dx.doi.org/10.1093/bioinformatics/bts183.

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22

TROUVÉ, S., F. RENAUD, P. DURAND, and J. JOURDANE. "Experimental evidence of hybrid breakdown between genetically distinct populations of Echinostoma caproni." Parasitology 117, no. 2 (August 1998): 133–35. http://dx.doi.org/10.1017/s0031182098002947.

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In this paper we investigate whether the assortative mating between individuals of the same genetic entity (i.e. coming from the same geographical area) of Echinostoma caproni, reported in a previous paper, may be explained by a post-zygotic isolating mechanism. The fecundity of the adults of 2 parental genetic entities and of their hybrids (i.e. F1, F2, F3) was quantified through 3 successive generations. Whereas the number of eggs released by F1 hybrids is similar to that of the mid-parent, that of recombinant hybrids (F2–3 hybrids) is significantly lower than that of F1 and that of the mid-parent. Since these results seem to demonstrate hybrid breakdown, 2 important factors maintaining reproductive isolation, i.e. pre-mating reproductive isolation and low hybrid fecundity, may influence the evolution of E. caproni.

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23

HABIB, MENA B., and MAURICE VAN KEULEN. "TwitterNEED: A hybrid approach for named entity extraction and disambiguation for tweet." Natural Language Engineering 22, no. 3 (July 10, 2015): 423–56. http://dx.doi.org/10.1017/s1351324915000194.

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AbstractTwitter is a rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing tasks. In this paper, we present TwitterNEED, a hybrid approach for Named Entity Extraction and Named Entity Disambiguation for tweets. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language and reduces error propagation in the whole system. Our extraction approach aims for high extraction recall first, after which a Support Vector Machine attempts to filter out false positives among the extracted candidates using features derived from the disambiguation phase in addition to other word shape and Knowledge Base features. For Named Entity Disambiguation, we obtain a list of entity candidates from the YAGO Knowledge Base in addition to top-ranked pages from the Google search engine for each extracted mention. We use a Support Vector Machine to rank the candidate pages according to a set of URL and context similarity features. For evaluation, five data sets are used to evaluate the extraction approach, and three of them to evaluate both the disambiguation approach and the combined extraction and disambiguation approach. Experiments show better results compared to our competitors DBpedia Spotlight, Stanford Named Entity Recognition, and the AIDA disambiguation system.

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van Dijk, Kor-jent, Michelle Waycott, Joe Quarmby, Doug Bickerton, Andrew H. Thornhill, Hugh Cross, and Edward Biffin. "Genomic Screening Reveals That the Endangered Eucalyptus paludicola (Myrtaceae) Is a Hybrid." Diversity 12, no. 12 (December 10, 2020): 468. http://dx.doi.org/10.3390/d12120468.

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A hybrid origin for a conservation listed taxon will influence its status and management options. Here, we investigate the genetic origins of a nationally endangered listed taxon—Eucalyptus paludicola—a tree that is restricted to the Fleurieu Peninsula and Kangaroo Island of South Australia. Since its description in 1995, there have been suggestions that this taxon may potentially be a stable hybrid species. Using a high throughput sequencing approach, we developed a panel of polymorphic loci that were screened across E. paludicola and its putative parental species E. cosmophylla and E. ovata. Bayesian clustering of the genotype data identified separate groups comprising E. ovata and E. cosmophylla while E. paludicola individuals were admixed between these two, consistent with a hybrid origin. Hybrid class assignment tests indicate that the majority of E. paludicola individuals (~70%) are F1 hybrids with a low incidence of backcrossing. Most of the post-F1 hybrids were associated with revegetation sites suggesting they may be maladapted and rarely reach maturity under natural conditions. These data support the hypothesis that E. paludicola is a transient hybrid entity rather than a distinct hybrid species. We briefly discuss the conservation implications of our findings.

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구성권. "A Study on the Taxation Issue of the Hybrid Mismatch - Focused on the Hybrid Entity and Hybrid Instrument -." Seoul Tax Law Review 22, no. 3 (November 2016): 541–84. http://dx.doi.org/10.16974/stlr.2016.22.3.012.

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26

Sharma, Richa, Sudha Morwal, and Basant Agarwal. "Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 3 (July 2021): 1–11. http://dx.doi.org/10.4018/ijcini.20210701.oa1.

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This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are promoted to a proposed model with the integration of CNN and conditional random field (CRF) layers. After a comparative analysis of results, it is verified that the performance of the proposed model (i.e., Bi-LSTM-CNN-CRF) is impressive. The proposed system achieves 61% precision, 56% recall, and 58% F-measure.

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Raja, Kalpana, Suresh Subramani, and Jeyakumar Natarajan. "A hybrid named entity tagger for tagging human proteins/genes." International Journal of Data Mining and Bioinformatics 10, no. 3 (2014): 315. http://dx.doi.org/10.1504/ijdmb.2014.064545.

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28

Milam, Sallie, and Melissa Moorehead. "Becoming a Hybrid Entity: A Policy Option for Public Health." Journal of Law, Medicine & Ethics 47, S2 (2019): 68–71. http://dx.doi.org/10.1177/1073110519857321.

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When Congress passed HIPAA, it did not intend to constrain public health's data sharing in the same way as clinical or payers. In fact, HIPAA recognizes data sharing with public health as a matter of national priority and shields this function from its reach. However, a health department may offer services that bring it within HIPAA's purview, such as running a Children's Health Insurance Program or a laboratory that bills electronically. When this is the case, HIPAA requires all information and departments be subject to HIPAA unless the public health authority chooses to hybridize. Health departments might re-assess their coverage and elect to become a hybrid entity, thereby restricting HIPAA to only where required and removing barriers to information sharing with communities.

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Talha, Meryem, Siham Boulaknadel, and Driss Aboutajdine. "Development of Amazighe Named Entity Recognition System Using Hybrid Method." Research in Computing Science 90, no. 1 (December 31, 2015): 151–61. http://dx.doi.org/10.13053/rcs-90-1-11.

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Andries, Marc, and Gregor Engels. "A Hybrid Query Language for an Extended Entity-Relationship Model." Journal of Visual Languages & Computing 7, no. 3 (September 1996): 321–52. http://dx.doi.org/10.1006/jvlc.1996.0017.

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Raza, Muhammad, Arsalan Ahmed, Jamshid Abdul-Ghafar, Rashida Ahmed, and Nasir Ud Din. "Hybrid odontogenic lesions: A case series of a rare entity." Heliyon 9, no. 5 (May 2023): e16221. http://dx.doi.org/10.1016/j.heliyon.2023.e16221.

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POIBEAU, THIERRY. "BOOSTING ROBUSTNESS OF A NAMED ENTITY RECOGNIZER." International Journal of Semantic Computing 03, no. 01 (March 2009): 91–104. http://dx.doi.org/10.1142/s1793351x09000689.

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Since the Message Understanding Conferences on Information Extraction in the 80's and 90's, Named Entity ReCognition (NERC) is a well-established task in the Natural Language Processing (NLP) community. However, very different systems seem to perform very similarly when applied to the same corpus. In this paper, we present a state-of-the-art NERC system. This tool is a hybrid system, based on different resources and techniques. We then propose a protocol to "deconstruct" and evaluate the different components of a complex named entity recognition system. We examine the performance of such a system with learning capacities and reduced initial knowledge on medium-size unlabelled corpora.

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Yujian, Wang, Tan Shaowei, Dong Weiwei, and Jing Wenpeng. "Research on 3D Modeling Method Based on Hybrid Octree Structure." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 323–29. http://dx.doi.org/10.2174/1874129001408010323.

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With studying deeply of the three-dimensional modeling method, this paper proposed a hybrid data model which based on Octree,the four fork tree and NURBS. The characteristic of fast convergence of Octree is used to segment the 3D entity. Describe the irregular surface of entity by NURBS, and restructure the local mesh surface. The model uses the mixture data structure of Octree and four fork tree to restructure mesh surface gradually. The storage structure is the Octree structure type; establish Hash table based on octal prefix code. Finally, an experimental model system is designed by using OpenGL. The feasibility and effectiveness of the algorithm has been verified.

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Gubina, Elena A. "Verbal-Pictorial Hybrids as Subject Matter of the Linguistic Study: on the Problem Statement." Proceedings of Southern Federal University. Philology 2021, no. 1 (March 30, 2021): 36–47. http://dx.doi.org/10.18522/1995-0640-2021-1-36-47.

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Verbal-pictorial hybrids are peculiar semiotic entities where the components of the verbal and pictorial sign systems are blended in such a way that they form an indissoluble entity and, thus, double their functional yield. Verbal-pictorial hybrids are kinds of сreolized (polycode) texts in which the convergence between verbal and pictorial components becomes the strongest and their formation is conditioned by the use of the same visual information transfer channel which is shared between both the written speech and images. The category in question includes calligrams, ASCIIart, pictorial puzzles, graphemic emoticons, specific forms of the graphic metaphor, marketing communication products (advertisements, logos) and all these give rise to a new unique category in the linguistic and semiotic studies. Verbal-pictorial hybrids belong to different discourses and genres and can be classified in accordance with their innate properties: antecedence/ subordinance of verbal and pictorial elements and, also, in accordance with the extent to which one of the semiotic dimensions loses its properties after becoming part of the hybrid entity.

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Chattopadhyay, Shital K., Indranil Kundu, and Ratnava Maitra. "The coumarin–pterocarpan conjugate – a natural product inspired hybrid molecular probe for DNA recognition." Org. Biomol. Chem. 12, no. 40 (2014): 8087–93. http://dx.doi.org/10.1039/c4ob01360c.

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Thermally induced cascade sigmatropic rearrangement of a butynyloxycoumarin derivative has led to a quick access to the coumarin–pterocarpan hybrid molecule. Biophysical studies together with molecular modeling show that this nature-inspired hybrid molecule is capable of binding to the minor groove of DNA as a non-conventional entity.

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Cheng, Yunru, Jianyi Guo, Yantuan Xian, Zhengtao Yu, Wei Chen, and Qiyue Yang. "A hybrid method for entity hyponymy acquisition in Chinese complex sentences." Automatic Control and Computer Sciences 50, no. 5 (September 2016): 369–77. http://dx.doi.org/10.3103/s0146411616050035.

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37

Liu, Jian, Lei Gao, Sujie Guo, Rui Ding, Xin Huang, Long Ye, Qinghua Meng, Asef Nazari, and Dhananjay Thiruvady. "A hybrid deep-learning approach for complex biochemical named entity recognition." Knowledge-Based Systems 221 (June 2021): 106958. http://dx.doi.org/10.1016/j.knosys.2021.106958.

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Karampatakis, Sotirios, Charalampos Bratsas, Ondřej Zamazal, Panagiotis Filippidis, and Ioannis Antoniou. "Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service." Information 9, no. 11 (November 15, 2018): 281. http://dx.doi.org/10.3390/info9110281.

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Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms.

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Zheng, Suncong, Yuexing Hao, Dongyuan Lu, Hongyun Bao, Jiaming Xu, Hongwei Hao, and Bo Xu. "Joint entity and relation extraction based on a hybrid neural network." Neurocomputing 257 (September 2017): 59–66. http://dx.doi.org/10.1016/j.neucom.2016.12.075.

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Wang, Yu, Yining Sun, Zuchang Ma, Lisheng Gao, and Yang Xu. "A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 2 (April 23, 2021): 1–12. http://dx.doi.org/10.1145/3436819.

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Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.

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Li, Zhucong, Zhen Gan, Baoli Zhang, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, and Shengping Liu. "Semi-Supervised Noisy Label Learning for Chinese Clinical Named Entity Recognition." Data Intelligence 3, no. 3 (2021): 389–401. http://dx.doi.org/10.1162/dint_a_00099.

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This paper describes our approach for the Chinese clinical named entity recognition (CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing (CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record (EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

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Liao, Carol. "Disruptive Innovation and the Global Emergence of Hybrid Corporate Legal Structures." European Company Law 11, Issue 2 (April 1, 2014): 67–70. http://dx.doi.org/10.54648/eucl2014012.

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A 'hybrid' is a corporate entity that embodies legal features which empower businesses to pursue dual economic and social mandates. This article explores how the global development of hybrid corporations have the potential to foster disruptive innovations in the marketplace that, in combination with top-down regulatory reform, will contribute to an environment where sustainable companies become the norm

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Kahlenberg, Christian. "Hybrid Entities: Problems Arising from the Attribution of Income Through Withholding Tax Relief Can Specific Domestic Provisions be a Suitable Solution Concept?" Intertax 44, Issue 2 (February 1, 2016): 146–62. http://dx.doi.org/10.54648/taxi2016010.

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The steadily increasing globalization of markets creates an immediate need to make adjustments to international tax law. At the same time, the lack of harmonization among national regulations fosters aspirations to achieve international tax arbitrage. In recent years, hybrid entity structures have been mentioned as effective means of tax avoidance or tax evasion. To respond to this development in a coordinated way, the BEPS project was initiated, and the OECD was put in charge to design viable solutions for a total of fifteen trouble spots that had been identified. The goal was to make sure that all states would act in sync. Recently – on October 5th – the final reports were published. Within the scope of Action 2, the particular focus is on hybrid entity structures. The present article uses the current developments at OECD level as an opportunity to scrutinize the addressed regulation for hybrid structures and to point out remaining loopholes. Subsequently, potential solutions are presented which also anticipate the risk of the treaty shopping.

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KE, SHIJIE, MENG-JIA ZHU, XUEWEI ZHAO, DING-KUN LIU, YUANYUAN LI, JIE HUANG, QING-HAI ZHANG, ZHONG-JIAN LIU, and SIREN LAN. "Cymbidium ×shangrilaense (Orchidaceae; Epidendroideae), a new natural hybrid from China: evidence from morphology and molecular analyses." Phytotaxa 561, no. 2 (September 8, 2022): 183–90. http://dx.doi.org/10.11646/phytotaxa.561.2.6.

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A new natural hybrid of Cymbidium (Orchidaceae), C. ×shangrilaense, from Yunnan Province, China, is illustrated and described. This entity may be a natural hybrid between C. tracyanum and C. gaoligongense. Morphologically, it differs from former by epidermal stripes and column and lip midlobe morphology and from the latter by its flower veins and lip morphology. Molecular analysis based on nuclear (ITS) DNA clearly indicated a relationship of the natural hybrid to group of Cymbidium species, whereas the plastid DNA (matK and rbcL) tree confirmed the natural hybrid as sister to C. tracyanum, C. subgenus Cyperorchis.

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Kung, Hung-Kai, Chun-Mo Hsieh, Cheng-Yu Ho, Yun-Cheng Tsai, Hao-Yung Chan, and Meng-Han Tsai. "Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning." Applied Sciences 10, no. 12 (June 20, 2020): 4234. http://dx.doi.org/10.3390/app10124234.

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This research aims to build a Mandarin named entity recognition (NER) module using transfer learning to facilitate damage information gathering and analysis in disaster management. The hybrid NER approach proposed in this research includes three modules: (1) data augmentation, which constructs a concise data set for disaster management; (2) reference model, which utilizes the bidirectional long short-term memory–conditional random field framework to implement NER; and (3) the augmented model built by integrating the first two modules via cross-domain transfer with disparate label sets. Through the combination of established rules and learned sentence patterns, the hybrid approach performs well in NER tasks for disaster management and recognizes unfamiliar words successfully. This research applied the proposed NER module to disaster management. In the application, we favorably handled the NER tasks of our related work and achieved our desired outcomes. Through proper transfer, the results of this work can be extended to other fields and consequently bring valuable advantages in diverse applications.

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Lara, Juan Antonio Herrero, Daniela de Araújo Martins-Romêo, Carlos Caparrós Escudero, Rosa María Lepe Vázquez, María del Carmen Prieto Falcón, and Vinicius Bianchi Batista. "Hybrid treatment of penetrating aortic ulcer." Radiologia Brasileira 48, no. 3 (June 2015): 192–94. http://dx.doi.org/10.1590/0100-3984.2014.0029.

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Abstract Penetrating atherosclerotic aortic ulcer is a rare entity with poor prognosis in the setting of acute aortic syndrome. In the literature, cases like the present one, located in the aortic arch, starting with chest pain and evolving with dysphonia, are even rarer. The present report emphasizes the role played by computed tomography in the diagnosis of penetrating atherosclerotic ulcer as well as in the differentiation of this condition from other acute aortic syndromes. Additionally, the authors describe a new therapeutic approach represented by a hybrid endovascular surgical procedure for treatment of the disease.

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Chiu, Jason P. C., and Eric Nichols. "Named Entity Recognition with Bidirectional LSTM-CNNs." Transactions of the Association for Computational Linguistics 4 (December 2016): 357–70. http://dx.doi.org/10.1162/tacl_a_00104.

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Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.

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Han, Youfang, and Chunping Li. "Entity Matching by Pool-Based Active Learning." Electronics 13, no. 3 (January 30, 2024): 559. http://dx.doi.org/10.3390/electronics13030559.

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The goal of entity matching is to find the corresponding records representing the same entity from different data sources. At present, in the mainstream methods, rule-based entity matching methods need tremendous domain knowledge. Machine-learning-based or deep-learning-based entity matching methods need a large number of labeled samples to build the model, which is difficult to achieve in some applications. In addition, learning-based methods are more likely to overfit, so the quality requirements of training samples are very high. In this paper, we present an active learning method for entity matching tasks. This method needs to manually label only a small number of valuable samples, and use these labeled samples to build a model with high quality. This paper proposes hybrid uncertainty as a query strategy to find those valuable samples for labeling, which can minimize the number of labeled training samples and at the same time meet the requirements of entity matching tasks. The proposed method is validated on seven data sets in different fields. The experiments show that the proposed method uses only a small number of labeled samples and achieves better effects compared to current existing approaches.

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Nasar, Zara, Syed Waqar Jaffry, and Muhammad Kamran Malik. "Named Entity Recognition and Relation Extraction." ACM Computing Surveys 54, no. 1 (April 2021): 1–39. http://dx.doi.org/10.1145/3445965.

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With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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S, Haroon Rasheed, Mohan Das S, and Samba Sivudu Gaddam. "DESIGN OF ENERGY EFFICIENT HYBRID 1-BIT FULL ADDER FOR ARITHMETIC APPLICATIONS." International Journal of Engineering Technology and Management Sciences 4, no. 6 (September 28, 2020): 15–18. http://dx.doi.org/10.46647/ijetms.2020.v04i06.004.

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This paper presents an energy efficient 1-bit full adder designed with a low voltage and high performance internal logic cells which leads to have abridged Power Delay Product (PDP). The customized XNOR and XOR gates, a necessary entity, are also presented. The simulations for the designed circuits performed in cadence virtuoso tool with 45-nm CMOS technology at a supply voltage of 0.9 Volts. The proposed 1-bit adder cell is compared with various trendy adders based on speed, power consumption and energy (PDP). The proposed adder schemes with modified internal entity cells achieve significant savings in terms of delay and energy consumption and which are more than 77% and 40.47% respectively when compared with conventional “C-CMOS” 1-bit full adder and other counter parts.

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