• 2017-2021close
• Part of book or chapter of book close
• CH close
• Archive ouverte UNIGE close
• Hyper Article en Ligne close
• Digital Humanities and Cultural Heritage close

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

France’s hesitant stance on EU enlargement towards the Balkans is illustrative of a broader ambivalence among both French elites and citizens towards the European project. Despite principled support for the Balkans’ EU membership, achieving this step is no strategic priority for France. The official approach emphasizes strict conditionality and a rigorous monitoring of reform progress in aspirant countries. A hostile public opinion and superficial media coverage further strengthen the country’s reluctance to admit new, possibly unprepared candidates into the Union. Analysing the historical evolution of the French position on EU enlargement as well as its current political, institutional and societal expressions, this article construes France’s disinvestment from the Balkans’ EU perspective as the result of failed expectations and a growing disillusionment with the EU’s international role and its political future more broadly. Southeast European and Black Sea Studies, 17 (4)

This paper offers an overview of the history of medial NP-adjuncts from Old English to Present-Day English. In Present-Day English, adverbs are perfectly grammatical in a position between the subject and the main verb ('He recently left for London') whereas NP-adjuncts are at best stylistically marked in this position ('(*)He tomorrow leaves for London'). The paper shows that while medial placement of NP-adjuncts has been considerably less frequent as compared to adverbs ever since around 1500, the contrast was initially much stronger in clauses with finite main verbs than in clauses with finite auxiliaries. It is only in the 19th century that medial placement becomes equally marked in both contexts. These developments are accounted for in terms of processing constraints disfavouring the use of medial NP-adjuncts and a structural reanalysis of NP-medial adjuncts in Late Modern English.

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

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.

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

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.

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.

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.