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27 Research products, page 1 of 3

  • 2017-2021
  • Conference object
  • CH
  • Archive ouverte UNIGE
  • Hyper Article en Ligne - Sciences de l'Homme et de la Société
  • Digital Humanities and Cultural Heritage

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  • Open Access English
    Authors: 
    Bugnon, Pascale; Matvienko, Alina;
    Country: Switzerland

    In the wake of the dissolution of the USSR, not all statues and other monuments dedicated to Lenin have suffered the same fate in the former Soviet republics. In Ukraine, for example, the “decommunisation” of the country meant that almost all the Soviet emblems were lost as collateral victims of the struggle to free themselves from the influence of the imposing Russian neighbour. In Central Asia, too, statues of Lenin have often been replaced by monuments to the new leaders, establishing their own cult of personality. In Kyrgyzstan, however, the memory of Lenin and his most famous statuary representation - the Lenin statue on Ala-Too Square in the centre of the city of Bishkek - has had a special destiny: untouched for over a decade after the collapse of communism, the monument was protected by a decree as a national heritage in 2000. And finally, when, in 2003, the government after all decided to remove the monument, it was then relocated only several meters from its original location. Far from signing its death, this relocation led to a re-reading of the monument and took on a plurality of uses in an unofficial register of representation. As symbols of a potentially controversial memory, the statues have regularly aroused strong “heritage emotions” (Fabre, 2013). In the wake of the claims expressed by the “Black Lives Matters” movement, this project proposes to examine the circumstances and forms of reappropriation of this particular statuary heritage. The importance of the monument as a referent in the rhetorical confrontations around power cannot be reduced to a clear-cut alternative between construction and destruction. From graffiti to decapitation and hijacking, citizens intervene in the public space to make claims, denounce, support or ignore. In the light of these repertoires of actions, we will analyse what the statues “say” or, rather, what they are made to say.

  • Publication . Article . Conference object . 2017
    Open Access English
    Authors: 
    Anne Obermann; Karyono Karyono; Tobias Diehl; Matteo Lupi; Adriano Mazzini;
    Countries: Germany, Switzerland
    Project: EC | IMAGE (608553), EC | LUSI LAB (308126), SNSF | Earthquake-induced fault ... (154815), SNSF | GENERATE - GEophysical an... (166900)

    We study the local seismicity in East Java around the Arjuno-Welirang volcanic complex that is connected via the Watukosek Fault System, to the spectacular Lusi eruption site. Lusi is a sediment-hosted hydrothermal system which has been erupting since 2006. It is fed by both mantellic and hydrothermal fluids, rising and mixing with the thermogenic gases and other fluids from shallower sedimentary formations. During a period of 24 months, we observe 156 micro-seismic earthquakes with local magnitudes ranging from ML0.5 to ML1.9, within our network. The events predominantly nucleate at depths of 8–13 km below the Arjuno-Welirang volcanic complex. Despite the geological evidence of active tectonic deformation and faulting observed at the surface, practically no seismicity is observed in the sedimentary basin hosting Lusi. Although we cannot entirely rule out artifacts due to an increased detection threshold in the sedimentary basin, the deficit in significant seismicity suggests aseismic deformation beneath Lusi due to the large amount of fluids that may lubricate the fault system. An analysis of focal mechanisms of nine selected events around the Arjuno-Welirang volcanic complex indicates predominantly strike-slip faulting activity in the region SW of Lusi. This type of activity is consistent with observable features such as fault escarpment, river deviation and railroad deformation; suggesting that the Watukosek fault system extends from the volcanic complex towards the NE of Java. Our results point out that the tectonic deformation of the region is characterized by a segmented fault system being part of a broader damage zone, rather than localized along a distinct fault plane. Obermann, Anne, et al. "Seismicity at Lusi and the adjacent volcanic complex, Java, Indonesia." Marine and Petroleum Geology (2017). Obermann, Anne, et al. "Seismicity at Lusi and the adjacent volcanic complex, Java, Indonesia." Marine and Petroleum Geology (2017). © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.

  • Publication . Article . Other literature type . Part of book or chapter of book . Conference object . Preprint . 2018
    Open Access English
    Authors: 
    Kristina Gulordava; Piotr Bojanowski; Edouard Grave; Tal Linzen; Marco Baroni;
    Publisher: Association for Computational Linguistics
    Country: Switzerland

    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. Accepted to NAACL 2018

  • Publication . Conference object . Part of book or chapter of book . 2017
    Open Access
    Authors: 
    Claudia Baur; Cathy Chua; Johanna Gerlach; Manny Rayner; Martin J. Russell; Helmer Strik; Xizi Wei;
    Publisher: ISCA
    Country: Switzerland

    We present an overview of the shared task for spoken CALL. Groups competed on a prompt-response task using English-language data collected, through an online CALL game, from Swiss German teens in their second and third years of learning English. Each item consists of a written German prompt and an audio file containing a spoken response. The task is to accept linguistically correct responses and reject linguistically incorrect ones, with “linguistically correct” being defined by a gold standard derived from human annotations; scoring was performed using a metric defined as the ratio of the relative rejection rates on incorrect and correct responses. The task received twenty entries from nine different groups. We present the task itself, the results, a tentative analysis of what makes items challenging, a comparison between different metrics, and suggestions for a continuation.

  • Publication . Part of book or chapter of book . Conference object . 2017
    Open Access
    Authors: 
    Achim Rabus; Yves Scherrer;
    Countries: Switzerland, Finland

    This paper reports on challenges and results in developing NLP resources for spoken Rusyn. Being a Slavic minority language, Rusyn does not have any resources to make use of. We propose to build a morphosyntactic dictionary for Rusyn, combining existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish. We adapt these resources to Rusyn by using vowel-sensitive Levenshtein distance, hand-written language-specific transformation rules, and combinations of the two. Compared to an exact match baseline, we increase the coverage of the resulting morphological dictionary by up to 77.4% relative (42.9% absolute), which results in a tagging recall increased by 11.6% relative (9.1% absolute). Our research confirms and expands the results of previous studies showing the efficiency of using NLP resources from neighboring languages for low-resourced languages. Peer reviewed

  • Publication . Conference object . Part of book or chapter of book . 2020
    Open Access
    Authors: 
    Elisa Terumi Rubel Schneider; João Vitor Andrioli de Souza; Julien Knafou; Lucas Emanuel Silva e Oliveira; Jenny Copara; Yohan Bonescki Gumiel; Lucas Ferro Antunes de Oliveira; Emerson Cabrera Paraiso; Douglas Teodoro; Claudia Maria Cabral Moro Barra;
    Publisher: Association for Computational Linguistics
    Country: Switzerland

    With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.

  • Publication . Other literature type . Conference object . Part of book or chapter of book . 2017
    Open Access
    Authors: 
    Marcos Zampieri; Shervin Malmasi; Nikola Ljubešić; Preslav Nakov; Ahmed Ali; Jörg Tiedemann; Yves Scherrer; Noëmi Aepli;
    Country: Switzerland

    We present the results of the VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part of the fourth edition of the VarDial workshop at EACL’2017. This year, we included four shared tasks: Discriminating between Similar Languages (DSL), Arabic Dialect Identification (ADI), German Dialect Identification (GDI), and Cross-lingual Dependency Parsing (CLP). A total of 19 teams submitted runs across the four tasks, and 15 of them wrote system description papers.

  • Publication . Conference object . Part of book or chapter of book . 2019
    Open Access
    Authors: 
    Alexandre Kabbach; Kristina Gulordava; Aurélie Herbelot;
    Publisher: Association for Computational Linguistics
    Country: Switzerland

    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.

  • Publication . Conference object . Other literature type . Part of book or chapter of book . 2021
    Open Access English
    Authors: 
    Marios Fanourakis; Guillaume Chanel; Rayan Elalamy; Phil Lopes;
    Publisher: IEEE
    Country: Switzerland

    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.

  • Publication . Conference object . Part of book or chapter of book . 2019
    Open Access
    Authors: 
    Simon Senecal; Niels A. Nijdam; Nadia Magnenat Thalmann;
    Publisher: SCITEPRESS - Science and Technology Publications
    Country: Switzerland

    Learning couple dance such as Salsa is a challenge for the modern human as it requires to assimilate and understand correctly all the dance parameters. Traditionally learned with a teacher, some situation and the variability of dance class environment can impact the learning process. Having a better understanding of what is a good salsa dancer from motion analysis perspective would bring interesting knowledge and can complement better learning. In this paper, we propose a set of music and interaction based motion features to classify salsa dancer couple performance in three learning states (beginner, intermediate and expert). These motion features are an interpretation of components given via interviews from teacher and professionals and other dance features found in systematic review of papers. For the presented study, a motion capture database (SALSA) has been recorded of 26 different couples with three skill levels dancing on 10 different tempos (260 clips). Each recorded clips con tains a basic steps sequence and an extended improvisation sequence during two minutes in total at 120 frame per second. Each of the 27 motion features have been computed on a sliding window that corresponds to the 8 beats reference for dance. Different multiclass classifier has been tested, mainly k-nearest neighbours, Random forest and Support Vector Machine, with an accuracy result of classification up to 81% for three levels and 92% for two levels. A later feature analysis validates 23 out of 27 proposed features. The work presented here has profound implications for future studies of motion analysis, couple dance learning and human-human interaction.

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