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Aiming at the problems of insufficient labelled samples and low-generalization performance in text classification tasks, this paper studies text classification problems under the condition of few labelled samples and proposes a few-shot short-text classification method (Meta-FCS) that combines the advantages of text semantic vector representation, meta-learning, fine-tuning and vector similarity measurement. The method not only effectively transfers the common features of different fields but also highlights the individual features of this field through fine-tuning. In addition, to facilitate the downstream text classification task, a deep language representation model is proposed. On this basis, the similarity between the query set and the class centroid of the support set is compared to determine the query set category. We evaluate the proposed method on a well-studied sentiment classification dataset, an entity-relationship classification dataset and an news topic dataset. The experimental results show that on these three datasets, the proposed method significantly outperforms the existing state-of-the-art approaches. It can thus be further suggested that the combination of deep language representation, episode training mechanism, and similarity measurement can be a promising solution for few-shot learning (FSL) of natural language processing (NLP) tasks. [ABSTRACT FROM AUTHOR]

Second-hand consumption is often seen as a way of reducing one’s ecological footprint. In an attempt to find out how the representations of second-hand consumption in discourse have evolved over time, a corpus study of the word second-hand is carried out on the basis of the Corpus of Historical American English (COHA) (1820–2019). Frequency is considered, as well as collocates, which show the types of second-hand items that are mentioned in different time periods and which indicate how positively or negatively connoted second-hand consumption is. Together with a more qualitative approach, this analysis reveals variation in the frequency and connotations of the word second-hand, and some long-lasting stigma which survives for example through comparisons. The article ends with some suggestions as to how the image of second-hand consumption could be improved.

This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment. [ABSTRACT FROM AUTHOR]

This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment.

In order to transition from a monolingual foreign language course to a multilingual one, all the elements that are connected to students’ cognition should be taken into consideration in order to facilitate this shift. Working with classes of French as a Foreign Language involving 11–12 years old Greek students, our study revealed that by switching to multilingual teaching, students’ representations of language(s) start to emerge; these representations had in most cases been hidden, incoherent and fossilized in teaching monolingual classes. While considering metalinguistic awareness, a variable that is central to our research, as a prerequisite for resolving problems emerging in a multilingual educational context, this article seeks to show that the representations language learners make of themselves and their learning constitute metalinguistic reflection at a macro level, which may influence how metalinguistic awareness functions when performing multilingual tasks. This paper focuses on the processing of qualitative data: meta-discourse analysis of the learners participating in our study led us to establish a typology of representations that enabled us to highlight what aspects to focus on in the classroom so as to prepare students to reflect on language more intensively. This involved guiding learners towards modifying their representational framework, by addressing deficiencies and correcting their representations characterized as “unproductive”, in order to take full advantage of multilingual teaching/learning situations.

In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients' discharge summaries and radiology reports. The extracted information can help in health-related decision making processes. The Named Entity Recognition (NER) task, which detects important concepts in texts (e.g., diseases, symptoms, drugs, etc.), is crucial in the information extraction process yet has received little attention in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narratives. We explore the use of contextualized word embeddings, which incorporate context variation into word representations, to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections extracted from open access Spanish-language medical publications. Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over a system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.

In this paper, we study the presence of French/Franco Provencal (FP) diglossia in FP literature, whether original texts or translations. We discuss: 1) the presence of French passages in ‘classical’ FP literature in the western part of the FP region; 2) the bilingual writing in the contemporary novel of the Savoyard writer Pierre Grasset and the differentiated possibilities offered by each language; 3) the staging of diglossia in the translation into Gruyère Francoprovençal (canton of Fribourg) of the Tintin album L’affaire Tournesol (Engl. The Calculus affair).

Copyright of Arab Journal for Arts / Journal of the Association of Arab Universities for Literature / Al-mağallaẗ Al-ʻarabiyyaẗ li-l-ādāb is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Synergies Pays Germanophones is the property of GERFLINT (Groupe d'Etudes et de Recherches pour le Francais Langue Internationale) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Within the framework of our research on learners' language practices and representations, this contribution explores how their representations about language uses and language learning shape the processes of learning and communication in school settings. More precisely, we will study learners' representations regarding the existence of a plurilingual asset, defined as plurilinguals' strategic advantage for further language learning and use. Following a discussion of the arguments that support the hypothesis of plurilingualism as a potential facilitator for both learning and verbal interaction, we will address the question of the sociolinguistic conditions that must be met for learners to perceive their plurilingualism as an asset in formal education. Special attention will be paid to the impact of a third language learnt at school on the construction of plurilingual repertoires and on the evolution of learners' representations about languages, language uses and learning as their repertoires widen and diversify. The population under study includes young plurilingual learners in two secondary schools and one university in France and Germany. The analysis of their discourse and underlying representations about the role of prior language knowledge in language behaviour and learning experience will allow us to discuss the role played by the institution in encouraging learners to view their plurilingualism as a strategic resource. [ABSTRACT FROM AUTHOR]