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description Publicationkeyboard_double_arrow_right Conference object 2021With the increasing adoption of technology, more and more systems become target to information security breaches. In terms of readily identifying zero-day vulnerabilities, a substantial number of news outlets and social media accounts reveal emerging vulnerabilities and threats. However, analysts often spend a lot of time looking through these decentralized sources of information in order to ensure up-to-date countermeasures and patches applicable to their organisation’s information systems. Various automated processing pipelines grounded in Natural Language Processing techniques for text classification were introduced for the early identification of vulnerabilities starting from Open-Source Intelligence (OSINT) data, including news websites, blogs, and social media. In this study, we consider a corpus of more than 1600 labeled news articles, and introduce an interpretable approach to the subject of cyberthreat early detection. In particular, an interpretable classification is performed using the Longformer architecture alongside prototypes from the ProSeNet structure, after performing a preliminary analysis on the Transformer’s encoding capabilities. The best interpretable architecture achieves an 88% F2-Score, arguing for the system’s applicability in real-life monitoring conditions of OSINT data.
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=10.26615/978-954-452-072-4_049&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 2 citations 2 popularity Average influence Average impulse Average 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=10.26615/978-954-452-072-4_049&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2020 United KingdomPublisher:International Committee on Computational Linguistics Funded by:EC | M and MEC| M and MAuthors: Mitchell, Jeffrey J; Bowers, Jeffrey S;Mitchell, Jeffrey J; Bowers, Jeffrey S;Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process. These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network. We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs. Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that underlines the non-human-like nature of the knowledge learned by the network.
Explore Bristol Rese... arrow_drop_down Explore Bristol Research; OpenAIREContribution for newspaper or weekly magazine . Conference object . 2020https://doi.org/10.18653/v1/20...Other literature type . Conference object . 2020 . Peer-reviewedadd 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=10.18653/v1/2020.coling-main.451&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 5 citations 5 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Explore Bristol Rese... arrow_drop_down Explore Bristol Research; OpenAIREContribution for newspaper or weekly magazine . Conference object . 2020https://doi.org/10.18653/v1/20...Other literature type . Conference object . 2020 . Peer-reviewedadd 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=10.18653/v1/2020.coling-main.451&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Other literature type , Part of book or chapter of book 2020Publisher:Springer International Publishing Funded by:EC | IASISEC| IASISAuthors: Oswaldo Solarte-Pabon; Ernestina Menasalvas; Alejandro Rodríguez-González;Oswaldo Solarte-Pabon; Ernestina Menasalvas; Alejandro Rodríguez-González;Electronic health records contain valuable information written in narrative form. A relevant challenge in clinical narrative text is that concepts commonly appear negated. Several proposals have been developed to detect negation in clinical text written in Spanish. Much of these proposals have adapted the Negex algorithm to Spanish, but obtained results indicating lower performance than NegEx implementations in other languages. Moreover, in most of these proposals, the validation process could be improved using a shared test corpus focused on negation in clinical text. This paper proposes Spa-neg, an approach to improve negation detection in clinical text written in Spanish. Spa-neg combines three elements: (i) an exploratory data analysis of how negation is written in the clinical text, (ii) use of regular expressions best adapted to the way in which negation is expressed in Spanish, (iii) experiments, and validation using a shared annotated corpus focused on negation. Our findings suggest that the combination of these elements improves the process of negation detection. The tests performed have shown 92% F-Score using IULA Spanish, an annotated corpus for negation in clinical text.
ZENODO arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2020 . Peer-reviewedLicense: Springer TDMData 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=10.1007/978-3-030-45385-5_29&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!visibility 160visibility views 160 download downloads 164 Powered bymore_vert ZENODO arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2020 . Peer-reviewedLicense: Springer TDMData 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=10.1007/978-3-030-45385-5_29&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publication2019 EnglishPublisher:Cognitive Computational Neuroscience Funded by:EC | M and MEC| M and MAuthors: Blything, Ryan; Vankov, Ivan; Ludwig, Casimir; Bowers, Jeffrey;Blything, Ryan; Vankov, Ivan; Ludwig, Casimir; Bowers, Jeffrey;OpenAIRE 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.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=10.32470/ccn.2019.1091-0&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert OpenAIRE 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.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=10.32470/ccn.2019.1091-0&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Presentation 2019Publisher:Figshare Authors: Demeranville, Tom; Olson, Eric; Minihan, Brian;Demeranville, Tom; Olson, Eric; Minihan, Brian;"Cooking with ORCID: Understanding and Sharing ORCID Connections" was presented by Eric Olson, Brian Minihan, and Tom Demeranville at the ORCID Consortia Workshop on May 20, 2019.
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=10.23640/07243.8184356&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average 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=10.23640/07243.8184356&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Presentation 2018 EnglishPublisher:Zenodo Funded by:EC | POSTDATAEC| POSTDATAAuthors: Ros; Salvador;Ros; Salvador;Presentation at the EADH 2018: "Data in Digital Humanities" at National University of Ireland, Galway 7-9 December 2018
ZENODO 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.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.2202995&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!visibility 53visibility views 53 download downloads 34 Powered bymore_vert ZENODO 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.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.2202995&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Contribution for newspaper or weekly magazine , Other literature type 2018 France, Switzerland, United KingdomPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMA, EC | HimL, EC | TraMOOC +2 projectsEC| SUMMA ,EC| HimL ,EC| TraMOOC ,SNSF| Rich Context in Neural Machine Translation ,SNSF| Dating structural fabric development using high spatial resolution 40Ar/39Ar geochronology: a Combined Filed and Experimental ApproachAuthors: Bawden, Rachel; Sennrich, Rico; Birch, Alexandra; Haddow, Barry;Bawden, Rachel; Sennrich, Rico; Birch, Alexandra; Haddow, Barry;For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50% accuracy on our coreference test set and 53.5% for coherence/cohesion (compared to a non-contextual baseline of 50%). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5% for coreference and 57% for coherence/cohesion), highlighting the importance of target-side context. Comment: Final version of paper to appear in Proceedings of NAACL 2018
OpenAIRE; Hal-Didero... arrow_drop_down OpenAIRE; Hal-DiderotConference object . 2018Hyper Article en Ligne; Mémoires en Sciences de l'Information et de la CommunicationOther literature type . Conference object . 2018Full-Text: https://hal.science/hal-01800739/documentZurich Open Repository and ArchiveConference object . 2018License: CC BYData sources: Zurich Open Repository and ArchiveEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2018Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2017Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2017License: 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=10.18653/v1/n18-1118&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 89 citations 89 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert OpenAIRE; Hal-Didero... arrow_drop_down OpenAIRE; Hal-DiderotConference object . 2018Hyper Article en Ligne; Mémoires en Sciences de l'Information et de la CommunicationOther literature type . Conference object . 2018Full-Text: https://hal.science/hal-01800739/documentZurich Open Repository and ArchiveConference object . 2018License: CC BYData sources: Zurich Open Repository and ArchiveEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2018Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2017Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2017License: 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=10.18653/v1/n18-1118&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2017 United KingdomPublisher:Association for Computational Linguistics (ACL) Funded by:EC | ENCASEEC| ENCASEJoan Serra; Ilias Leontiadis; Dimitris Spathis; Gianluca Stringhini; Jeremy Blackburn; Athena Vakali;Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ability to describe seen documents to the ability to predict unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4–11%
https://www.aclweb.o... 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.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=10.18653/v1/w17-3005&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 20 citations 20 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!visibility 69visibility views 69 download downloads 52 Powered bymore_vert https://www.aclweb.o... 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.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=10.18653/v1/w17-3005&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Other literature type 2017 United Kingdom, SwitzerlandPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMAEC| SUMMALiepins, R; Germann, U; Barzdins, G; Birch, A; Renals, S; Weber, S; Van Der Kreeft, P; Bourlard, H; Prieto, J; Klejch, O; Bell, P; Lazaridis, A; Mendes, A; Riedel, S; Almeida, MSC; Balage, P; Cohen, S; Dwojak, T; Garner, P; Giefer, A; Junczys-Dowmunt, M; Imran, H; Nogueira, D; Ali, A; Miranda, S; Popescu-Belis, A; Werlen, LM; Papasarantopoulos, N; Obamuyide, A; Jones, C; Dalvi, F; Vlachos, A; Wang, Y; Tong, S; Sennrich, R; Pappas, N; Narayan, S; Damonte, M; Durrani, N; Khurana, S; Abdelali, A; Sajjad, H; Vogel, S; Sheppey, D; Hernon, C; Mitchell, J;We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.
UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd 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=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 46visibility views 46 download downloads 49 Powered bymore_vert UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd 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=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article , Contribution for newspaper or weekly magazine 2017 United KingdomPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMA, EC | QT21EC| SUMMA ,EC| QT21Authors: Sennrich, Rico;Sennrich, Rico;Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval97, a large-scale data set of 97000 contrastive translation pairs based on the WMT English->German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning. accepted at EACL 2017 (v3: minor fix to table 6 description)
https://www.aclweb.o... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2016Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2016License: 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=10.18653/v1/e17-2060&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 12 citations 12 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert https://www.aclweb.o... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2016Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2016License: 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=10.18653/v1/e17-2060&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Conference object 2021With the increasing adoption of technology, more and more systems become target to information security breaches. In terms of readily identifying zero-day vulnerabilities, a substantial number of news outlets and social media accounts reveal emerging vulnerabilities and threats. However, analysts often spend a lot of time looking through these decentralized sources of information in order to ensure up-to-date countermeasures and patches applicable to their organisation’s information systems. Various automated processing pipelines grounded in Natural Language Processing techniques for text classification were introduced for the early identification of vulnerabilities starting from Open-Source Intelligence (OSINT) data, including news websites, blogs, and social media. In this study, we consider a corpus of more than 1600 labeled news articles, and introduce an interpretable approach to the subject of cyberthreat early detection. In particular, an interpretable classification is performed using the Longformer architecture alongside prototypes from the ProSeNet structure, after performing a preliminary analysis on the Transformer’s encoding capabilities. The best interpretable architecture achieves an 88% F2-Score, arguing for the system’s applicability in real-life monitoring conditions of OSINT data.
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=10.26615/978-954-452-072-4_049&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 2 citations 2 popularity Average influence Average impulse Average 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=10.26615/978-954-452-072-4_049&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2020 United KingdomPublisher:International Committee on Computational Linguistics Funded by:EC | M and MEC| M and MAuthors: Mitchell, Jeffrey J; Bowers, Jeffrey S;Mitchell, Jeffrey J; Bowers, Jeffrey S;Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process. These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network. We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs. Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that underlines the non-human-like nature of the knowledge learned by the network.
Explore Bristol Rese... arrow_drop_down Explore Bristol Research; OpenAIREContribution for newspaper or weekly magazine . Conference object . 2020https://doi.org/10.18653/v1/20...Other literature type . Conference object . 2020 . Peer-reviewedadd 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=10.18653/v1/2020.coling-main.451&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 5 citations 5 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Explore Bristol Rese... arrow_drop_down Explore Bristol Research; OpenAIREContribution for newspaper or weekly magazine . Conference object . 2020https://doi.org/10.18653/v1/20...Other literature type . Conference object . 2020 . Peer-reviewedadd 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=10.18653/v1/2020.coling-main.451&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Other literature type , Part of book or chapter of book 2020Publisher:Springer International Publishing Funded by:EC | IASISEC| IASISAuthors: Oswaldo Solarte-Pabon; Ernestina Menasalvas; Alejandro Rodríguez-González;Oswaldo Solarte-Pabon; Ernestina Menasalvas; Alejandro Rodríguez-González;Electronic health records contain valuable information written in narrative form. A relevant challenge in clinical narrative text is that concepts commonly appear negated. Several proposals have been developed to detect negation in clinical text written in Spanish. Much of these proposals have adapted the Negex algorithm to Spanish, but obtained results indicating lower performance than NegEx implementations in other languages. Moreover, in most of these proposals, the validation process could be improved using a shared test corpus focused on negation in clinical text. This paper proposes Spa-neg, an approach to improve negation detection in clinical text written in Spanish. Spa-neg combines three elements: (i) an exploratory data analysis of how negation is written in the clinical text, (ii) use of regular expressions best adapted to the way in which negation is expressed in Spanish, (iii) experiments, and validation using a shared annotated corpus focused on negation. Our findings suggest that the combination of these elements improves the process of negation detection. The tests performed have shown 92% F-Score using IULA Spanish, an annotated corpus for negation in clinical text.
ZENODO arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2020 . Peer-reviewedLicense: Springer TDMData 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=10.1007/978-3-030-45385-5_29&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!visibility 160visibility views 160 download downloads 164 Powered bymore_vert ZENODO arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2020 . Peer-reviewedLicense: Springer TDMData 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=10.1007/978-3-030-45385-5_29&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publication2019 EnglishPublisher:Cognitive Computational Neuroscience Funded by:EC | M and MEC| M and MAuthors: Blything, Ryan; Vankov, Ivan; Ludwig, Casimir; Bowers, Jeffrey;Blything, Ryan; Vankov, Ivan; Ludwig, Casimir; Bowers, Jeffrey;OpenAIRE 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.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=10.32470/ccn.2019.1091-0&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert OpenAIRE 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.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=10.32470/ccn.2019.1091-0&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Presentation 2019Publisher:Figshare Authors: Demeranville, Tom; Olson, Eric; Minihan, Brian;Demeranville, Tom; Olson, Eric; Minihan, Brian;"Cooking with ORCID: Understanding and Sharing ORCID Connections" was presented by Eric Olson, Brian Minihan, and Tom Demeranville at the ORCID Consortia Workshop on May 20, 2019.
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=10.23640/07243.8184356&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average 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=10.23640/07243.8184356&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Presentation 2018 EnglishPublisher:Zenodo Funded by:EC | POSTDATAEC| POSTDATAAuthors: Ros; Salvador;Ros; Salvador;Presentation at the EADH 2018: "Data in Digital Humanities" at National University of Ireland, Galway 7-9 December 2018
ZENODO 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.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.2202995&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!visibility 53visibility views 53 download downloads 34 Powered bymore_vert ZENODO 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.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.2202995&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Contribution for newspaper or weekly magazine , Other literature type 2018 France, Switzerland, United KingdomPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMA, EC | HimL, EC | TraMOOC +2 projectsEC| SUMMA ,EC| HimL ,EC| TraMOOC ,SNSF| Rich Context in Neural Machine Translation ,SNSF| Dating structural fabric development using high spatial resolution 40Ar/39Ar geochronology: a Combined Filed and Experimental ApproachAuthors: Bawden, Rachel; Sennrich, Rico; Birch, Alexandra; Haddow, Barry;Bawden, Rachel; Sennrich, Rico; Birch, Alexandra; Haddow, Barry;For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50% accuracy on our coreference test set and 53.5% for coherence/cohesion (compared to a non-contextual baseline of 50%). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5% for coreference and 57% for coherence/cohesion), highlighting the importance of target-side context. Comment: Final version of paper to appear in Proceedings of NAACL 2018
OpenAIRE; Hal-Didero... arrow_drop_down OpenAIRE; Hal-DiderotConference object . 2018Hyper Article en Ligne; Mémoires en Sciences de l'Information et de la CommunicationOther literature type . Conference object . 2018Full-Text: https://hal.science/hal-01800739/documentZurich Open Repository and ArchiveConference object . 2018License: CC BYData sources: Zurich Open Repository and ArchiveEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2018Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2017Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2017License: 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=10.18653/v1/n18-1118&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 89 citations 89 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert OpenAIRE; Hal-Didero... arrow_drop_down OpenAIRE; Hal-DiderotConference object . 2018Hyper Article en Ligne; Mémoires en Sciences de l'Information et de la CommunicationOther literature type . Conference object . 2018Full-Text: https://hal.science/hal-01800739/documentZurich Open Repository and ArchiveConference object . 2018License: CC BYData sources: Zurich Open Repository and ArchiveEdinburgh Research ExplorerContribution for newspaper or weekly magazine . 2018Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2017Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2017License: 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=10.18653/v1/n18-1118&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2017 United KingdomPublisher:Association for Computational Linguistics (ACL) Funded by:EC | ENCASEEC| ENCASEJoan Serra; Ilias Leontiadis; Dimitris Spathis; Gianluca Stringhini; Jeremy Blackburn; Athena Vakali;Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ability to describe seen documents to the ability to predict unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4–11%
https://www.aclweb.o... 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.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=10.18653/v1/w17-3005&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 20 citations 20 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!visibility 69visibility views 69 download downloads 52 Powered bymore_vert https://www.aclweb.o... 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.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=10.18653/v1/w17-3005&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Other literature type 2017 United Kingdom, SwitzerlandPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMAEC| SUMMALiepins, R; Germann, U; Barzdins, G; Birch, A; Renals, S; Weber, S; Van Der Kreeft, P; Bourlard, H; Prieto, J; Klejch, O; Bell, P; Lazaridis, A; Mendes, A; Riedel, S; Almeida, MSC; Balage, P; Cohen, S; Dwojak, T; Garner, P; Giefer, A; Junczys-Dowmunt, M; Imran, H; Nogueira, D; Ali, A; Miranda, S; Popescu-Belis, A; Werlen, LM; Papasarantopoulos, N; Obamuyide, A; Jones, C; Dalvi, F; Vlachos, A; Wang, Y; Tong, S; Sennrich, R; Pappas, N; Narayan, S; Damonte, M; Durrani, N; Khurana, S; Abdelali, A; Sajjad, H; Vogel, S; Sheppey, D; Hernon, C; Mitchell, J;We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.
UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd 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=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 46visibility views 46 download downloads 49 Powered bymore_vert UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd 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=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article , Contribution for newspaper or weekly magazine 2017 United KingdomPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMA, EC | QT21EC| SUMMA ,EC| QT21Authors: Sennrich, Rico;Sennrich, Rico;Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval97, a large-scale data set of 97000 contrastive translation pairs based on the WMT English->German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning. accepted at EACL 2017 (v3: minor fix to table 6 description)
https://www.aclweb.o... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2016Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2016License: 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=10.18653/v1/e17-2060&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 12 citations 12 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert https://www.aclweb.o... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2017Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2016Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2016License: 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=10.18653/v1/e17-2060&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu