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description Publicationkeyboard_double_arrow_right Conference object , Part of book or chapter of book 2014 SwitzerlandPublisher:Association for Computational Linguistics and Dublin City University Authors: Scherrer, Yves;Scherrer, Yves;doi: 10.3115/v1/w14-5304
When developing NLP tools for low-resource languages, one is often confronted with the lack of annotated data. We propose to circumvent this bottleneck by training a supervised HMM tagger on a closely related language for which annotated data are available, and translating the words in the tagger parameter files into the low-resource language. The translation dictionaries are created with unsupervised lexicon induction techniques that rely only on raw textual data. We obtain a tagging accuracy of up to 89.08% using a Spanish tagger adapted to Catalan, which is 30.66% above the performance of an unadapted Spanish tagger, and 8.88% below the performance of a supervised tagger trained on annotated Catalan data. Furthermore, we evaluate our model on several Romance, Germanic and Slavic languages and obtain tagging accuracies of up to 92%.
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=10.3115/v1/w14-5304&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu6 citations 6 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=10.3115/v1/w14-5304&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Conference object , Part of book or chapter of book 2016 SwitzerlandPublisher:Association for Computational Linguistics (ACL) Rayner, Manny; Bouillon, Pierrette; Ebling, Sarah; Gerlach, Johanna; Strasly, Irene; Tsourakis, Nikos;We present an open web platform for developing, compiling, and running rulebased speech to sign language translation applications. Speech recognition is performed using the Nuance Recognizer 10.2 toolkit, and signed output, including both manual and non-manual components, is rendered using the JASigning avatar system. The platform is designed to make the component technologies readily accessible to sign language experts who are not necessarily computer scientists. Translation grammars are written in a version of Synchronous Context-Free Grammar adapted to the peculiarities of sign language. All processing is carried out on a remote server, with content uploaded and accessed through a web interface. Initial experiences show that simple translation grammars can be implemented on a time-scale of a few hours to a few days and produce signed output readily comprehensible to Deaf informants. Overall, the platform drastically lowers the barrier to entry for researchers interested in building applications that generate high-quality signed language.
Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2016Data sources: Zurich Open Repository and Archiveadd 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/p16-2027&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 6 citations 6 popularity Average influence Average impulse Average Powered by BIP!more_vert Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2016Data sources: Zurich Open Repository and Archiveadd 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/p16-2027&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Part of book or chapter of book 2017 Netherlands, SwitzerlandPublisher:ISCA Baur, C.; Chua, C.; Gerlach, J.; Rayner, E.; Russell, M.; Strik, H.; Wei, X.; Engwall, O.; Lopes, J.; Leite, I.;handle: 2066/187674
7th ISCA Workshop on Speech and Language Technology in Education, 25 augustus 2017 Item does not contain fulltext
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=10.21437/slate.2017-13&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average 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=10.21437/slate.2017-13&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Part of book or chapter of book 2015 SwitzerlandPublisher:ACM Funded by:SNSF | Annotation Automatique de...SNSF| Annotation Automatique de Documents pour les besoins du Web SémantiqueAuthors: Nebhi, Kamel; Bontcheva, Kalina; Gorrell, Genevieve;Nebhi, Kamel; Bontcheva, Kalina; Gorrell, Genevieve;The rapid proliferation of microblogs such as Twitter has resulted in a vast quantity of written text becoming available that contains interesting information for NLP tasks. However, the noise level in tweets is so high that standard NLP tools perform poorly. In this pa- per, we present a statistical truecaser for tweets using a 3-gram language model built with truecased newswire texts and tweets. Our truecasing method shows an improvement in named entity recognition and part-of-speech tagging tasks.
https://archive-ouve... arrow_drop_down https://doi.org/10.1145/274090...Conference object . 2015 . Peer-reviewedLicense: ACM Copyright Policiesadd 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=10.1145/2740908.2743039&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu11 citations 11 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert https://archive-ouve... arrow_drop_down https://doi.org/10.1145/274090...Conference object . 2015 . Peer-reviewedLicense: ACM Copyright Policiesadd 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=10.1145/2740908.2743039&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Part of book or chapter of book , Conference object , Preprint 2018 SwitzerlandPublisher:Association for Computational Linguistics (ACL) Gulordava, Kristina; Bojanowski, Piotr; Grave, Edouard; Linzen, Tal; Baroni, Marco;Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues ("The colorless green ideas I ate with the chair sleep furiously"), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence. Comment: Accepted to NAACL 2018
Archive ouverte UNIG... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2018License: 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/n18-1108&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 199 citations 199 popularity Top 1% influence Top 1% impulse Top 0.1% Powered by BIP!more_vert Archive ouverte UNIG... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2018License: 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/n18-1108&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Part of book or chapter of book 2019 Italy, SwitzerlandPublisher:Association for Computational Linguistics (ACL) Authors: Kabbach, Alexandre; Gulordava, Kristina; Herbelot, Aurelie;Kabbach, Alexandre; Gulordava, Kristina; Herbelot, Aurelie;doi: 10.18653/v1/p19-2022
handle: 11572/249669
In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. We focus on the notion of ‘informativeness', that is, the idea that some content is more valuable to the learning process than other. We further highlight the challenges of online learning and argue that previous systems fall short of implementing incrementality. Concretely, we incorporate informativeness in a previously proposed model of nonce learning, using it for context selection and learning rate modulation. We test our system on the task of learning new words from definitions, as well as on the task of learning new words from potentially uninformative contexts. We demonstrate that informativeness is crucial to obtaining state-of-the-art performance in a truly incremental setup.
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/p19-2022&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 5 citations 5 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=10.18653/v1/p19-2022&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 2021 SwitzerlandPublisher:IEEE Authors: Fanourakis, Marios Aristogenis; Chanel, Guillaume; Elalamy, Rayan; Lopes, Phil;Fanourakis, Marios Aristogenis; Chanel, Guillaume; Elalamy, Rayan; Lopes, Phil;Emotion recognition is usually achieved by collecting features (physiological signals, events, facial expressions, etc.) to predict an emotional ground truth. This ground truth is arguably unreliable due to its subjective nature. In this paper, we introduce a new approach to measure the magnitude of an emotion in the latent space of a Neural Network without the need for a subjective ground truth. Our data consists of physiological measurements during video gameplay, game events, and subjective rankings of game events for the validation of our model. Our model encodes physiological features into a latent variable which is then decoded into video game events. We show that the events are ranked in the latent space similarly to the participants' subjective ranks. For instance, our model's ranking is correlated (Kendall $\tau$ of 0.91) with the predictability rankings.
Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1109/percom...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData 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=10.1109/percomworkshops51409.2021.9430963&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 Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1109/percom...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData 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=10.1109/percomworkshops51409.2021.9430963&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Preprint , Article 2016 SwitzerlandPublisher:Association for Computational Linguistics (ACL) Authors: Henderson, James; Popa, Diana Nicoleta;Henderson, James; Popa, Diana Nicoleta;Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributional-semantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results. To appear in Proc. 54th Annual Meeting of the Association Computational Linguistics (ACL 2016)
Archive ouverte UNIG... arrow_drop_down https://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/p16-1193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 11 citations 11 popularity Average influence Average impulse Top 10% Powered by BIP!more_vert Archive ouverte UNIG... arrow_drop_down https://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/p16-1193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 2017 Malta, SwitzerlandPublisher:IEEE Authors: Lopes, Phil; Yannakakis, Georgios N.; Liapis, Antonios; International Conference on Affective Computing and Intelligent Interaction (ACII) 2017;Lopes, Phil; Yannakakis, Georgios N.; Liapis, Antonios; International Conference on Affective Computing and Intelligent Interaction (ACII) 2017;How should annotation data be processed so that it can best characterize the ground truth of affect? This paper attempts to address this critical question by testing various methods of processing annotation data on their ability to capture phasic elements of skin conductance. Towards this goal the paper introduces a new affect annotation tool, RankTrace, that allows for the annotation of affect in a continuous yet unbounded fashion. RankTrace is tested on first-person annotation lines (traces) of tension elicited from a horror video game. The key findings of the paper suggest that the relative processing of traces via their mean gradient yields the best and most robust predictors of phasic manifestations of skin conductance. peer-reviewed
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=10.1109/acii.2017.8273594&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu32 citations 32 popularity Top 10% 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=10.1109/acii.2017.8273594&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book 2016 SwitzerlandPublisher:Springer International Publishing Funded by:SNSF | TepalcatlSNSF| TepalcatlAuthors: Roman Rangel, Edgar Francisco; Jimenez-Badillo, Diego; Marchand-Maillet, Stéphane;Roman Rangel, Edgar Francisco; Jimenez-Badillo, Diego; Marchand-Maillet, Stéphane;We introduce a method for estimation of rotation invariant local shape descriptors for 3D models. This method follows a successful idea commonly used to obtain rotation invariant descriptors in 2D images, and improves it by tackling the difficulty of the 3 degrees of freedom that exists in 3D models. Our method is simple, yet it achieves high levels of invariance after rotation transformations, and it produces short descriptors that can be efficiently used in several tasks. Such is the case of automatic classification of 3D surfaces with archaeological value, in which the proposed method attains state-of-the-art results in shorter times when compared with previous methods.
Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: Springer TDMadd 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=10.1007/978-3-319-39393-3_2&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 Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: Springer TDMadd 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=10.1007/978-3-319-39393-3_2&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Conference object , Part of book or chapter of book 2014 SwitzerlandPublisher:Association for Computational Linguistics and Dublin City University Authors: Scherrer, Yves;Scherrer, Yves;doi: 10.3115/v1/w14-5304
When developing NLP tools for low-resource languages, one is often confronted with the lack of annotated data. We propose to circumvent this bottleneck by training a supervised HMM tagger on a closely related language for which annotated data are available, and translating the words in the tagger parameter files into the low-resource language. The translation dictionaries are created with unsupervised lexicon induction techniques that rely only on raw textual data. We obtain a tagging accuracy of up to 89.08% using a Spanish tagger adapted to Catalan, which is 30.66% above the performance of an unadapted Spanish tagger, and 8.88% below the performance of a supervised tagger trained on annotated Catalan data. Furthermore, we evaluate our model on several Romance, Germanic and Slavic languages and obtain tagging accuracies of up to 92%.
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=10.3115/v1/w14-5304&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu6 citations 6 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=10.3115/v1/w14-5304&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Conference object , Part of book or chapter of book 2016 SwitzerlandPublisher:Association for Computational Linguistics (ACL) Rayner, Manny; Bouillon, Pierrette; Ebling, Sarah; Gerlach, Johanna; Strasly, Irene; Tsourakis, Nikos;We present an open web platform for developing, compiling, and running rulebased speech to sign language translation applications. Speech recognition is performed using the Nuance Recognizer 10.2 toolkit, and signed output, including both manual and non-manual components, is rendered using the JASigning avatar system. The platform is designed to make the component technologies readily accessible to sign language experts who are not necessarily computer scientists. Translation grammars are written in a version of Synchronous Context-Free Grammar adapted to the peculiarities of sign language. All processing is carried out on a remote server, with content uploaded and accessed through a web interface. Initial experiences show that simple translation grammars can be implemented on a time-scale of a few hours to a few days and produce signed output readily comprehensible to Deaf informants. Overall, the platform drastically lowers the barrier to entry for researchers interested in building applications that generate high-quality signed language.
Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2016Data sources: Zurich Open Repository and Archiveadd 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/p16-2027&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 6 citations 6 popularity Average influence Average impulse Average Powered by BIP!more_vert Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2016Data sources: Zurich Open Repository and Archiveadd 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/p16-2027&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Part of book or chapter of book 2017 Netherlands, SwitzerlandPublisher:ISCA Baur, C.; Chua, C.; Gerlach, J.; Rayner, E.; Russell, M.; Strik, H.; Wei, X.; Engwall, O.; Lopes, J.; Leite, I.;handle: 2066/187674
7th ISCA Workshop on Speech and Language Technology in Education, 25 augustus 2017 Item does not contain fulltext
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=10.21437/slate.2017-13&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average 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=10.21437/slate.2017-13&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Part of book or chapter of book 2015 SwitzerlandPublisher:ACM Funded by:SNSF | Annotation Automatique de...SNSF| Annotation Automatique de Documents pour les besoins du Web SémantiqueAuthors: Nebhi, Kamel; Bontcheva, Kalina; Gorrell, Genevieve;Nebhi, Kamel; Bontcheva, Kalina; Gorrell, Genevieve;The rapid proliferation of microblogs such as Twitter has resulted in a vast quantity of written text becoming available that contains interesting information for NLP tasks. However, the noise level in tweets is so high that standard NLP tools perform poorly. In this pa- per, we present a statistical truecaser for tweets using a 3-gram language model built with truecased newswire texts and tweets. Our truecasing method shows an improvement in named entity recognition and part-of-speech tagging tasks.
https://archive-ouve... arrow_drop_down https://doi.org/10.1145/274090...Conference object . 2015 . Peer-reviewedLicense: ACM Copyright Policiesadd 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=10.1145/2740908.2743039&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu11 citations 11 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert https://archive-ouve... arrow_drop_down https://doi.org/10.1145/274090...Conference object . 2015 . Peer-reviewedLicense: ACM Copyright Policiesadd 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=10.1145/2740908.2743039&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Part of book or chapter of book , Conference object , Preprint 2018 SwitzerlandPublisher:Association for Computational Linguistics (ACL) Gulordava, Kristina; Bojanowski, Piotr; Grave, Edouard; Linzen, Tal; Baroni, Marco;Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues ("The colorless green ideas I ate with the chair sleep furiously"), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence. Comment: Accepted to NAACL 2018
Archive ouverte UNIG... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2018License: 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/n18-1108&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 199 citations 199 popularity Top 1% influence Top 1% impulse Top 0.1% Powered by BIP!more_vert Archive ouverte UNIG... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2018License: 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/n18-1108&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Part of book or chapter of book 2019 Italy, SwitzerlandPublisher:Association for Computational Linguistics (ACL) Authors: Kabbach, Alexandre; Gulordava, Kristina; Herbelot, Aurelie;Kabbach, Alexandre; Gulordava, Kristina; Herbelot, Aurelie;doi: 10.18653/v1/p19-2022
handle: 11572/249669
In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. We focus on the notion of ‘informativeness', that is, the idea that some content is more valuable to the learning process than other. We further highlight the challenges of online learning and argue that previous systems fall short of implementing incrementality. Concretely, we incorporate informativeness in a previously proposed model of nonce learning, using it for context selection and learning rate modulation. We test our system on the task of learning new words from definitions, as well as on the task of learning new words from potentially uninformative contexts. We demonstrate that informativeness is crucial to obtaining state-of-the-art performance in a truly incremental setup.
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/p19-2022&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 5 citations 5 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=10.18653/v1/p19-2022&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 2021 SwitzerlandPublisher:IEEE Authors: Fanourakis, Marios Aristogenis; Chanel, Guillaume; Elalamy, Rayan; Lopes, Phil;Fanourakis, Marios Aristogenis; Chanel, Guillaume; Elalamy, Rayan; Lopes, Phil;Emotion recognition is usually achieved by collecting features (physiological signals, events, facial expressions, etc.) to predict an emotional ground truth. This ground truth is arguably unreliable due to its subjective nature. In this paper, we introduce a new approach to measure the magnitude of an emotion in the latent space of a Neural Network without the need for a subjective ground truth. Our data consists of physiological measurements during video gameplay, game events, and subjective rankings of game events for the validation of our model. Our model encodes physiological features into a latent variable which is then decoded into video game events. We show that the events are ranked in the latent space similarly to the participants' subjective ranks. For instance, our model's ranking is correlated (Kendall $\tau$ of 0.91) with the predictability rankings.
Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1109/percom...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData 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=10.1109/percomworkshops51409.2021.9430963&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 Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1109/percom...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData 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=10.1109/percomworkshops51409.2021.9430963&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Preprint , Article 2016 SwitzerlandPublisher:Association for Computational Linguistics (ACL) Authors: Henderson, James; Popa, Diana Nicoleta;Henderson, James; Popa, Diana Nicoleta;Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributional-semantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results. To appear in Proc. 54th Annual Meeting of the Association Computational Linguistics (ACL 2016)
Archive ouverte UNIG... arrow_drop_down https://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/p16-1193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 11 citations 11 popularity Average influence Average impulse Top 10% Powered by BIP!more_vert Archive ouverte UNIG... arrow_drop_down https://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/p16-1193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 2017 Malta, SwitzerlandPublisher:IEEE Authors: Lopes, Phil; Yannakakis, Georgios N.; Liapis, Antonios; International Conference on Affective Computing and Intelligent Interaction (ACII) 2017;Lopes, Phil; Yannakakis, Georgios N.; Liapis, Antonios; International Conference on Affective Computing and Intelligent Interaction (ACII) 2017;How should annotation data be processed so that it can best characterize the ground truth of affect? This paper attempts to address this critical question by testing various methods of processing annotation data on their ability to capture phasic elements of skin conductance. Towards this goal the paper introduces a new affect annotation tool, RankTrace, that allows for the annotation of affect in a continuous yet unbounded fashion. RankTrace is tested on first-person annotation lines (traces) of tension elicited from a horror video game. The key findings of the paper suggest that the relative processing of traces via their mean gradient yields the best and most robust predictors of phasic manifestations of skin conductance. peer-reviewed
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=10.1109/acii.2017.8273594&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu32 citations 32 popularity Top 10% 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=10.1109/acii.2017.8273594&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book 2016 SwitzerlandPublisher:Springer International Publishing Funded by:SNSF | TepalcatlSNSF| TepalcatlAuthors: Roman Rangel, Edgar Francisco; Jimenez-Badillo, Diego; Marchand-Maillet, Stéphane;Roman Rangel, Edgar Francisco; Jimenez-Badillo, Diego; Marchand-Maillet, Stéphane;We introduce a method for estimation of rotation invariant local shape descriptors for 3D models. This method follows a successful idea commonly used to obtain rotation invariant descriptors in 2D images, and improves it by tackling the difficulty of the 3 degrees of freedom that exists in 3D models. Our method is simple, yet it achieves high levels of invariance after rotation transformations, and it produces short descriptors that can be efficiently used in several tasks. Such is the case of automatic classification of 3D surfaces with archaeological value, in which the proposed method attains state-of-the-art results in shorter times when compared with previous methods.
Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: Springer TDMadd 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=10.1007/978-3-319-39393-3_2&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 Archive ouverte UNIG... arrow_drop_down https://doi.org/10.1007/978-3-...Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: Springer TDMadd 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=10.1007/978-3-319-39393-3_2&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu