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Almost entirely overlooked throughout the 20th century, neglected by contemporary medical manuals, the clitoris has gradually returned centre stage thanks to Western feminism.

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 In Central Asia, too, statues of Lenin have often been replaced by monuments to the new leaders, establishing their own cult of personality.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.

We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. HIPE-2022 is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of the evaluation lab is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets.

Abstract The recent developments of the Liège intranuclear cascade model INCL are reviewed. The INCL4.6 version of this model was able when coupled with the ABLA07 de-excitation code, to describe rather well a huge set of experimental data in an incident energy range spanning between 200 MeV and 3 GeV, as it has been testified by an intercomparison of spallation codes organized by the IAEA. Since that time, the model has been implemented in several nuclear particle transport codes. Therefore, the possible applications of INCL have been enlarged to focus on diverse fields, and in the recent years, the model has been further developed to be applicable to these new issues and also to cope with remaining deficiencies. The new features include: i) a sophisticated dynamical model for light cluster emission (up to O ions), ii) the accommodation of light nuclei as projectiles, iii) a new procedure to take account of the fuzziness of the Fermi surface, and iv) an extension of the model to higher energy. The aim of this contribution is to present for the first time and to discuss the physics of the added features, and to give a hint about the performances of the new model.

We consider methods for handling incomplete (elliptical) utterances in spoken phraselators, and describe how they have been implemented inside BabelDr, a substantial spoken medical phraselator. The challenge is to extend the phrase matching process so that it is sensitive to preceding dialogue context. We contrast two methods, one using limited-vocabulary strict grammar-based speech and language processing and one using large-vocabulary speech recognition with fuzzy grammar-based processing, and present an initial evaluation on a spoken corpus of 821 context-sentence/elliptical-phrase pairs. The large-vocabulary/fuzzy method strongly outperforms the limited-vocabulary/strict method over the whole corpus, though it is slightly inferior for the subset that is within grammar coverage. We investigate possibilities for combining the two processing paths, using several machine learning frameworks, and demonstrate that hybrid methods strongly outperform the large-vocabulary/fuzzy method.

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

Web accessibility has only recently begun to be considered as a key component in the task of the web localiser and, crucially, in the assessment of localisation quality. The ALMA research project (Approaching Localisation by Means of Accessibility) seeks to address this gap by gradually but comprehensively introducing accessibility awareness, issues and perspectives in the principles and procedures of localisation. One of the approaches of ALMA focuses on localiser education and aims at both integrating web accessibility as content to be transferred in the process of localisation and as a methodological way of rethinking website analysis and interlingual, intercultural, intersemiotic transformation. This would allow localisation students to observe the interrelation between the different semiotic, temporal, spatial or ergodic elements coded in the product, with the aim of being perceived, understood and operated by users through different modalities, senses, capacities and technologies. In this chapter, the specific example of culture and heritage websites is used to illustrate how the social and technological dimensions of multimodal translation, localisation and accessibility converge. By exploring the interrelation of web accessibility, localiser education, Universal Design for Learning, and culture and heritage websites, we conclude that such combination can provide a critical opportunity to enhance accessibility and learning at various levels: as an outcome of localisation training (more accessible multilingual culture and heritage websites), as a motivational driver for all students to access and be engaged in education, as an accessibility-aware mindset and methodology (better and deeper access to training materials), as well as an excellent interdisciplinary tool.

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.

Ce chapitre recense les exemples Genevois d'ouvertures cadavériques privées. En explorant ces cas, il interroge les modalités et les raisons de ces investigations post-mortems. Quel sens pouvait-il y avoir pour les proches de connaître "la cause" d'une mort particulière? Cet intérêt pour la connaissance de la pathologie ne rapproche-t-elle pas les considérations des laïcs de celles des médecins?

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.