- home
- Advanced Search
- Digital Humanities and Cultural Heritage
- 2017-2021
- Publications
- Preprint
- IE
- arXiv.org e-Print Archive
- Hal-Diderot
- Digital Humanities and Cultural Heritage
- 2017-2021
- Publications
- Preprint
- IE
- arXiv.org e-Print Archive
- Hal-Diderot
Loading
description Publicationkeyboard_double_arrow_right Preprint , Article 2021Publisher:Elsevier BV Funded by:National Council for Forest Research and DevelopmentNational Council for Forest Research and DevelopmentAuthors: de Rosa, Gustavo Henrique; Papa, João Paulo;de Rosa, Gustavo Henrique; Papa, João Paulo;handle: 11449/229013
Abstract This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called “natural” language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.patcog.2021.108098&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 34 citations 34 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.patcog.2021.108098&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2021Publisher:ACM Wahed, Muntasir; Gruhl, Daniel; Alba, Alfredo; Gentile, Anna Lisa; Ristoski, Petar; Deluca, Chad; Welch, Steve; Lourentzou, Ismini;Recent advances in text representation have shown that training on large amounts of text is crucial for natural language understanding. However, models trained without predefined notions of topical interest typically require careful fine-tuning when transferred to specialized domains. When a sufficient amount of within-domain text may not be available, expanding a seed corpus of relevant documents from large-scale web data poses several challenges. First, corpus expansion requires scoring and ranking each document in the collection, an operation that can quickly become computationally expensive as the web corpora size grows. Relying on dense vector spaces and pairwise similarity adds to the computational expense. Secondly, as the domain concept becomes more nuanced, capturing the long tail of domain-specific rare terms becomes non-trivial, especially under limited seed corpora scenarios. In this paper, we consider the problem of fast approximate corpus expansion given a small seed corpus with a few relevant documents as a query, with the goal of capturing the long tail of a domain-specific set of concept terms. To efficiently collect large-scale domain-specific corpora with limited relevance feedback, we propose a novel truncated sparse document bit-vector representation, termed Signature Assisted Unsupervised Corpus Expansion (SAUCE). Experimental results show that SAUCE can reduce the computational burden while ensuring high within-domain lexical coverage. Accepted to CIKM'21 Applied Research Track
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3459637.3481950&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3459637.3481950&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2021 IrelandPublisher:ACM Publicly fundedAuthors: Krishna, Tarun; McGuinness, Kevin; O'Connor, Noel E.;Krishna, Tarun; McGuinness, Kevin; O'Connor, Noel E.;In this work, we evaluate contrastive models for the task of image retrieval. We hypothesise that models that are learned to encode semantic similarity among instances via discriminative learning should perform well on the task of image retrieval, where relevancy is defined in terms of instances of the same object. Through our extensive evaluation, we find that representations from models trained using contrastive methods perform on-par with (and outperforms) a pre-trained supervised baseline trained on the ImageNet labels in retrieval tasks under various configurations. This is remarkable given that the contrastive models require no explicit supervision. Thus, we conclude that these models can be used to bootstrap base models to build more robust image retrieval engines. Comment: Accepted In Proceedings of the 2021 International Conference on Multimedia Retrieval (ICMR 21)
DCU Online Research ... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access ServiceDCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Serviceadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3460426.3463585&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert DCU Online Research ... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access ServiceDCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Serviceadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3460426.3463585&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint , Research 2021 Netherlands, United Kingdom, BelgiumPublisher:Zenodo Publicly fundedFunded by:EC | PREP-IBISBA, SSHRC, EC | BioExcel-2 +5 projectsEC| PREP-IBISBA ,SSHRC ,EC| BioExcel-2 ,EC| IBISBA 1.0 ,EC| RELIANCE ,EC| EOSC-Life ,EC| SYNTHESYS PLUS ,EC| BY-COVIDStian Soiland-Reyes; Peter Sefton; Mercè Crosas; Leyla Jael Castro; Frederik Coppens; José M. Fernández; Daniel Garijo; Björn Grüning; Marco La Rosa; Simone Leo; Eoghan Ó Carragáin; Marc Portier; Ana Trisovic; RO-Crate Community; Paul Groth; Carole Goble;An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema$.$org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://www.researchobject.org/2021-packaging-research-artefacts-with-ro-crate/ Comment: 42 pages. Submitted to Data Science
NARCIS; Data Science arrow_drop_down ZENODO; The University of Manchester - Institutional RepositoryOther literature type . Article . 2022 . 2021License: CC BYGhent University Academic BibliographyArticle . 2022Data sources: Ghent University Academic Bibliographyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5146228&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 23 citations 23 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!visibility 720visibility views 720 download downloads 624 Powered bymore_vert NARCIS; Data Science arrow_drop_down ZENODO; The University of Manchester - Institutional RepositoryOther literature type . Article . 2022 . 2021License: CC BYGhent University Academic BibliographyArticle . 2022Data sources: Ghent University Academic Bibliographyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5146228&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Conference object , Article 2021Publisher:ACM Srinivasan, Krishna; Raman, Karthik; Chen, Jiecao; Bendersky, Michael; Najork, Marc;The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality visio-linguistic datasets for learning complementary information across image and text modalities. In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.5 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example. WIT Dataset is available for download and use via a Creative Commons license here: https://github.com/google-research-datasets/wit.
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3404835.3463257&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 39 citations 39 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3404835.3463257&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2021Publisher:Springer Science and Business Media LLC Publicly fundedFunded by:Dublin City University, SFI | ADAPT: Centre for Digital...Dublin City University ,SFI| ADAPT: Centre for Digital Content Platform ResearchAuthors: Cortis, Keith; Davis, Brian;Cortis, Keith; Davis, Brian;Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications. Comment: 170 pages, 3 figures. This is a preprint of an article published in Artificial Intelligence Review (2021)
Artificial Intellige... arrow_drop_down Artificial Intelligence ReviewArticle . 2021Full-Text: http://europepmc.org/articles/PMC8227369Data sources: PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10462-021-10030-2&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 31 citations 31 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Artificial Intellige... arrow_drop_down Artificial Intelligence ReviewArticle . 2021Full-Text: http://europepmc.org/articles/PMC8227369Data sources: PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10462-021-10030-2&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2021Publisher:Springer Science and Business Media LLC Publicly fundedFunded by:IRC, EC | ELEXIS, EC | Pret-a-LLODIRC ,EC| ELEXIS ,EC| Pret-a-LLODAuthors: Chakravarthi, Bharathi Raja; Rani, Priya; Arcan, Mihael; McCrae, John P.;Chakravarthi, Bharathi Raja; Rani, Priya; Arcan, Mihael; McCrae, John P.;Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable, but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography's influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts of cognates information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction. 18 pages
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8550410Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s42979-021-00723-4&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 34visibility views 34 download downloads 22 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8550410Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s42979-021-00723-4&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Preprint 2021 Spain, IrelandPublisher:IEEE Publicly fundedFonseca, Eduardo; Ortego, Diego; McGuinness, Kevin; O’Connor, Noel E.; Serra, Xavier;Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound event research. In this work, we explore unsupervised contrastive learning as a way to learn sound event representations. To this end, we propose to use the pretext task of contrasting differently augmented views of sound events. The views are computed primarily via mixing of training examples with unrelated backgrounds, followed by other data augmentations. We analyze the main components of our method via ablation experiments. We evaluate the learned representations using linear evaluation, and in two in-domain downstream sound event classification tasks, namely, using limited manually labeled data, and using noisy labeled data. Our results suggest that unsupervised contrastive pre-training can mitigate the impact of data scarcity and increase robustness against noisy labels, outperforming supervised baselines. A 4-page version is submitted to ICASSP 2021
UPF Digital Reposito... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Servicehttps://doi.org/10.1109/icassp...Conference object . 2021 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icassp39728.2021.9415009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert UPF Digital Reposito... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Servicehttps://doi.org/10.1109/icassp...Conference object . 2021 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icassp39728.2021.9415009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint , Other literature type 2021 FrancePublisher:Cambridge University Press (CUP) Publicly fundedFunded by:ANR | PGSEANR| PGSEAuthors: Denis Cogneau; Yannick Dupraz; Sandrine Mesplé-Somps;Denis Cogneau; Yannick Dupraz; Sandrine Mesplé-Somps;International audience; What was the capacity of European colonial states? How fiscally extractive were they? What was their capacity to provide public goods and services? And did this change in the “developmentalist” era of colonialism? To answer these questions, we use archival sources to build a new dataset on colonial states of the second French colonial empire (1830-1962). French colonial states extracted a substantial amount of revenue, but they were under-administered because public expenditure entailed high wage costs. These costs remained a strong constraint in the “developmentalist” era of colonialism, despite a dramatic increase in fiscal capacity and large overseas subsidies.
The Journal of Econo... arrow_drop_down The Journal of Economic HistoryArticle . 2021 . Peer-reviewedLicense: Cambridge Core User AgreementData sources: CrossrefHAL - UPEC / UPEM; HAL-Pasteur; HAL-InsermPreprint . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1017/s0022050721000140&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert The Journal of Econo... arrow_drop_down The Journal of Economic HistoryArticle . 2021 . Peer-reviewedLicense: Cambridge Core User AgreementData sources: CrossrefHAL - UPEC / UPEM; HAL-Pasteur; HAL-InsermPreprint . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1017/s0022050721000140&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2021Publisher:Elsevier BV Publicly fundedFunded by:EC | CONNECTING Nature, UKRI | SustAinable BiOdegradable...EC| CONNECTING Nature ,UKRI| SustAinable BiOdegradable Tooling SABOTMohammadhossein Ghahramani; Nadina J. Galle; Fábio Duarte; Carlo Ratti; Francesco Pilla;Abstract Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people’s opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform’s reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of–and justification for–future investments in urban green space.
City and Environment... arrow_drop_down City and Environment InteractionsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.cacint.2021.100058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert City and Environment... arrow_drop_down City and Environment InteractionsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.cacint.2021.100058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
Loading
description Publicationkeyboard_double_arrow_right Preprint , Article 2021Publisher:Elsevier BV Funded by:National Council for Forest Research and DevelopmentNational Council for Forest Research and DevelopmentAuthors: de Rosa, Gustavo Henrique; Papa, João Paulo;de Rosa, Gustavo Henrique; Papa, João Paulo;handle: 11449/229013
Abstract This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called “natural” language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.patcog.2021.108098&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 34 citations 34 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.patcog.2021.108098&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2021Publisher:ACM Wahed, Muntasir; Gruhl, Daniel; Alba, Alfredo; Gentile, Anna Lisa; Ristoski, Petar; Deluca, Chad; Welch, Steve; Lourentzou, Ismini;Recent advances in text representation have shown that training on large amounts of text is crucial for natural language understanding. However, models trained without predefined notions of topical interest typically require careful fine-tuning when transferred to specialized domains. When a sufficient amount of within-domain text may not be available, expanding a seed corpus of relevant documents from large-scale web data poses several challenges. First, corpus expansion requires scoring and ranking each document in the collection, an operation that can quickly become computationally expensive as the web corpora size grows. Relying on dense vector spaces and pairwise similarity adds to the computational expense. Secondly, as the domain concept becomes more nuanced, capturing the long tail of domain-specific rare terms becomes non-trivial, especially under limited seed corpora scenarios. In this paper, we consider the problem of fast approximate corpus expansion given a small seed corpus with a few relevant documents as a query, with the goal of capturing the long tail of a domain-specific set of concept terms. To efficiently collect large-scale domain-specific corpora with limited relevance feedback, we propose a novel truncated sparse document bit-vector representation, termed Signature Assisted Unsupervised Corpus Expansion (SAUCE). Experimental results show that SAUCE can reduce the computational burden while ensuring high within-domain lexical coverage. Accepted to CIKM'21 Applied Research Track
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3459637.3481950&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3459637.3481950&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2021 IrelandPublisher:ACM Publicly fundedAuthors: Krishna, Tarun; McGuinness, Kevin; O'Connor, Noel E.;Krishna, Tarun; McGuinness, Kevin; O'Connor, Noel E.;In this work, we evaluate contrastive models for the task of image retrieval. We hypothesise that models that are learned to encode semantic similarity among instances via discriminative learning should perform well on the task of image retrieval, where relevancy is defined in terms of instances of the same object. Through our extensive evaluation, we find that representations from models trained using contrastive methods perform on-par with (and outperforms) a pre-trained supervised baseline trained on the ImageNet labels in retrieval tasks under various configurations. This is remarkable given that the contrastive models require no explicit supervision. Thus, we conclude that these models can be used to bootstrap base models to build more robust image retrieval engines. Comment: Accepted In Proceedings of the 2021 International Conference on Multimedia Retrieval (ICMR 21)
DCU Online Research ... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access ServiceDCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Serviceadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3460426.3463585&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 3 citations 3 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert DCU Online Research ... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access ServiceDCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Serviceadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3460426.3463585&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint , Research 2021 Netherlands, United Kingdom, BelgiumPublisher:Zenodo Publicly fundedFunded by:EC | PREP-IBISBA, SSHRC, EC | BioExcel-2 +5 projectsEC| PREP-IBISBA ,SSHRC ,EC| BioExcel-2 ,EC| IBISBA 1.0 ,EC| RELIANCE ,EC| EOSC-Life ,EC| SYNTHESYS PLUS ,EC| BY-COVIDStian Soiland-Reyes; Peter Sefton; Mercè Crosas; Leyla Jael Castro; Frederik Coppens; José M. Fernández; Daniel Garijo; Björn Grüning; Marco La Rosa; Simone Leo; Eoghan Ó Carragáin; Marc Portier; Ana Trisovic; RO-Crate Community; Paul Groth; Carole Goble;An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema$.$org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://www.researchobject.org/2021-packaging-research-artefacts-with-ro-crate/ Comment: 42 pages. Submitted to Data Science
NARCIS; Data Science arrow_drop_down ZENODO; The University of Manchester - Institutional RepositoryOther literature type . Article . 2022 . 2021License: CC BYGhent University Academic BibliographyArticle . 2022Data sources: Ghent University Academic Bibliographyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5146228&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 23 citations 23 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!visibility 720visibility views 720 download downloads 624 Powered bymore_vert NARCIS; Data Science arrow_drop_down ZENODO; The University of Manchester - Institutional RepositoryOther literature type . Article . 2022 . 2021License: CC BYGhent University Academic BibliographyArticle . 2022Data sources: Ghent University Academic Bibliographyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5146228&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Conference object , Article 2021Publisher:ACM Srinivasan, Krishna; Raman, Karthik; Chen, Jiecao; Bendersky, Michael; Najork, Marc;The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality visio-linguistic datasets for learning complementary information across image and text modalities. In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.5 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example. WIT Dataset is available for download and use via a Creative Commons license here: https://github.com/google-research-datasets/wit.
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3404835.3463257&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 39 citations 39 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3404835.3463257&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2021Publisher:Springer Science and Business Media LLC Publicly fundedFunded by:Dublin City University, SFI | ADAPT: Centre for Digital...Dublin City University ,SFI| ADAPT: Centre for Digital Content Platform ResearchAuthors: Cortis, Keith; Davis, Brian;Cortis, Keith; Davis, Brian;Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications. Comment: 170 pages, 3 figures. This is a preprint of an article published in Artificial Intelligence Review (2021)
Artificial Intellige... arrow_drop_down Artificial Intelligence ReviewArticle . 2021Full-Text: http://europepmc.org/articles/PMC8227369Data sources: PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10462-021-10030-2&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 31 citations 31 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Artificial Intellige... arrow_drop_down Artificial Intelligence ReviewArticle . 2021Full-Text: http://europepmc.org/articles/PMC8227369Data sources: PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10462-021-10030-2&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2021Publisher:Springer Science and Business Media LLC Publicly fundedFunded by:IRC, EC | ELEXIS, EC | Pret-a-LLODIRC ,EC| ELEXIS ,EC| Pret-a-LLODAuthors: Chakravarthi, Bharathi Raja; Rani, Priya; Arcan, Mihael; McCrae, John P.;Chakravarthi, Bharathi Raja; Rani, Priya; Arcan, Mihael; McCrae, John P.;Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable, but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography's influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts of cognates information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction. 18 pages
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8550410Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s42979-021-00723-4&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 34visibility views 34 download downloads 22 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8550410Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s42979-021-00723-4&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Preprint 2021 Spain, IrelandPublisher:IEEE Publicly fundedFonseca, Eduardo; Ortego, Diego; McGuinness, Kevin; O’Connor, Noel E.; Serra, Xavier;Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound event research. In this work, we explore unsupervised contrastive learning as a way to learn sound event representations. To this end, we propose to use the pretext task of contrasting differently augmented views of sound events. The views are computed primarily via mixing of training examples with unrelated backgrounds, followed by other data augmentations. We analyze the main components of our method via ablation experiments. We evaluate the learned representations using linear evaluation, and in two in-domain downstream sound event classification tasks, namely, using limited manually labeled data, and using noisy labeled data. Our results suggest that unsupervised contrastive pre-training can mitigate the impact of data scarcity and increase robustness against noisy labels, outperforming supervised baselines. A 4-page version is submitted to ICASSP 2021
UPF Digital Reposito... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Servicehttps://doi.org/10.1109/icassp...Conference object . 2021 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icassp39728.2021.9415009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert UPF Digital Reposito... arrow_drop_down DCU Online Research Access ServiceConference object . 2021 . Peer-reviewedData sources: DCU Online Research Access Servicehttps://doi.org/10.1109/icassp...Conference object . 2021 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icassp39728.2021.9415009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint , Other literature type 2021 FrancePublisher:Cambridge University Press (CUP) Publicly fundedFunded by:ANR | PGSEANR| PGSEAuthors: Denis Cogneau; Yannick Dupraz; Sandrine Mesplé-Somps;Denis Cogneau; Yannick Dupraz; Sandrine Mesplé-Somps;International audience; What was the capacity of European colonial states? How fiscally extractive were they? What was their capacity to provide public goods and services? And did this change in the “developmentalist” era of colonialism? To answer these questions, we use archival sources to build a new dataset on colonial states of the second French colonial empire (1830-1962). French colonial states extracted a substantial amount of revenue, but they were under-administered because public expenditure entailed high wage costs. These costs remained a strong constraint in the “developmentalist” era of colonialism, despite a dramatic increase in fiscal capacity and large overseas subsidies.
The Journal of Econo... arrow_drop_down The Journal of Economic HistoryArticle . 2021 . Peer-reviewedLicense: Cambridge Core User AgreementData sources: CrossrefHAL - UPEC / UPEM; HAL-Pasteur; HAL-InsermPreprint . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1017/s0022050721000140&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert The Journal of Econo... arrow_drop_down The Journal of Economic HistoryArticle . 2021 . Peer-reviewedLicense: Cambridge Core User AgreementData sources: CrossrefHAL - UPEC / UPEM; HAL-Pasteur; HAL-InsermPreprint . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1017/s0022050721000140&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2021Publisher:Elsevier BV Publicly fundedFunded by:EC | CONNECTING Nature, UKRI | SustAinable BiOdegradable...EC| CONNECTING Nature ,UKRI| SustAinable BiOdegradable Tooling SABOTMohammadhossein Ghahramani; Nadina J. Galle; Fábio Duarte; Carlo Ratti; Francesco Pilla;Abstract Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people’s opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform’s reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of–and justification for–future investments in urban green space.
City and Environment... arrow_drop_down City and Environment InteractionsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.cacint.2021.100058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 21 citations 21 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert City and Environment... arrow_drop_down City and Environment InteractionsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.cacint.2021.100058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu