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Clear Alldescription Publicationkeyboard_double_arrow_right Article , Preprint 2020Embargo end date: 01 Jan 2020arXiv Zamani, Maryam; Tejedor, Alejandro; Vogl, Malte; Krautli, Florian; Valleriani, Matteo; Kantz, Holger;We investigated the evolution and transformation of scientific knowledge in the early modern period, analyzing more than 350 different editions of textbooks used for teaching astronomy in European universities from the late fifteenth century to mid-seventeenth century. These historical sources constitute the Sphaera Corpus. By examining different semantic relations among individual parts of each edition on record, we built a multiplex network consisting of six layers, as well as the aggregated network built from the superposition of all the layers. The network analysis reveals the emergence of five different communities. The contribution of each layer in shaping the communities and the properties of each community are studied. The most influential books in the corpus are found by calculating the average age of all the out-going and in-coming links for each book. A small group of editions is identified as a transmitter of knowledge as they bridge past knowledge to the future through a long temporal interval. Our analysis, moreover, identifies the most disruptive books. These books introduce new knowledge that is then adopted by almost all the books published afterwards until the end of the whole period of study. The historical research on the content of the identified books, as an empirical test, finally corroborates the results of all our analyses. Comment: 19 pages, 9 figures
arXiv.org e-Print Ar... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.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=10.48550/arxiv.2004.00520&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average 
Powered by BIP!description Publicationkeyboard_double_arrow_right Conference object 2020 Malta EnglishNicholas, Lionel; Lyding, Verena; Borg, Claudia; Forascu, Corina; Fort, Karen; Zdravkova, Katerina; Kosem, Iztok; Cibej, Jaka; Holdt, Spela Arhar; Millour, Alice; Konig, Alexander; Rodosthenous, Christos; Sangati, Federico; Hassan, Umair ul; Katinskaia, Anisia; Barreiro, Anabela; Aparaschivei, Lavina; HaCohen-Kerner, Yaakov; 12th edition of the Language Resources and Evaluation Conference (LREC'20);We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of the generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs. peer-reviewed
Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.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=od______3549::752584a3b9e9a9af52902efa6aa71c70&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu
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description Publicationkeyboard_double_arrow_right Article , Preprint 2020Embargo end date: 01 Jan 2020arXiv Zamani, Maryam; Tejedor, Alejandro; Vogl, Malte; Krautli, Florian; Valleriani, Matteo; Kantz, Holger;We investigated the evolution and transformation of scientific knowledge in the early modern period, analyzing more than 350 different editions of textbooks used for teaching astronomy in European universities from the late fifteenth century to mid-seventeenth century. These historical sources constitute the Sphaera Corpus. By examining different semantic relations among individual parts of each edition on record, we built a multiplex network consisting of six layers, as well as the aggregated network built from the superposition of all the layers. The network analysis reveals the emergence of five different communities. The contribution of each layer in shaping the communities and the properties of each community are studied. The most influential books in the corpus are found by calculating the average age of all the out-going and in-coming links for each book. A small group of editions is identified as a transmitter of knowledge as they bridge past knowledge to the future through a long temporal interval. Our analysis, moreover, identifies the most disruptive books. These books introduce new knowledge that is then adopted by almost all the books published afterwards until the end of the whole period of study. The historical research on the content of the identified books, as an empirical test, finally corroborates the results of all our analyses. Comment: 19 pages, 9 figures
arXiv.org e-Print Ar... arrow_drop_down 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.Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.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=10.48550/arxiv.2004.00520&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!description Publicationkeyboard_double_arrow_right Conference object 2020 Malta EnglishNicholas, Lionel; Lyding, Verena; Borg, Claudia; Forascu, Corina; Fort, Karen; Zdravkova, Katerina; Kosem, Iztok; Cibej, Jaka; Holdt, Spela Arhar; Millour, Alice; Konig, Alexander; Rodosthenous, Christos; Sangati, Federico; Hassan, Umair ul; Katinskaia, Anisia; Barreiro, Anabela; Aparaschivei, Lavina; HaCohen-Kerner, Yaakov; 12th edition of the Language Resources and Evaluation Conference (LREC'20);We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of the generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs. peer-reviewed
Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.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=od______3549::752584a3b9e9a9af52902efa6aa71c70&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu