Sign languages play a crucial role in the communication of deaf communities, but they are often marginalized, limiting access to essential services such as healthcare and education. This study proposes an automatic sign language recognition system based on a hybrid CNN-LSTM architecture, using Mediapipe for gesture keypoint extraction. Developed with Python, TensorFlow and Streamlit, the system provides real-time gesture translation. The results show an average accuracy of 92\%, with very good performance for distinct gestures such as ``Hello'' and ``Thank you''. However, some confusions remain for visually similar gestures, such as ``Call'' and ``Yes''. This work opens up interesting perspectives for applications in various fields such as healthcare, education and public services.

Animating a Knowledge Base: From Ontologies to Generative AI Models From Expert Systems and the Semantic W eb to Generative AI: Model - Driven and Data - Driven Approaches in Area Studies In a context where the social sciences and humanities are experimenting with non - anthropocentric analytical frames, this article proposes a semiotic (structural) reading of the hybridization between symbolic AI and neural (or sub - symbolic) AI based on a field of application: the design and use of a knowledge base for area studies. W e describe the LaCAS ecosystem - Open Archives in Linguistic and Cultural Studies (thesaurus; RDF/OWL ontology; LOD services; harvesting; expertise; publication), deployed at Inalco (National Institute for Oriental Languages and Civilizations) in Paris with the Okapi (Open Knowledge and Annotation Interface) software environment from Ina (National Audiovisual Institute), which now has around 160,000 documentary r esources and ten knowledge macro - domains grouping together several thousand knowledge objects. W e illustrate this approach using the knowledge domain "Languages of the world" (~540 languages) and the knowledge object "Quechua (language)". On this basis, we discuss the controlled integration of neural tools, more specifically generative tools, into the life cycle of a knowledge base: assistance with data localization/qualification, index extraction and aggregation, property suggestion and testing, dynamic file generation, and engineering of contextualized prompts (generic, contextual, explanatory, adjustment, procedural) aligned with a domain ontology. W e outline an ecosystem of specialized agents capable of animating the database while respe cting its symbolic constraints, by articulating model - driven and data - driven methods .

Deaf people are more heavily a ffected by the digital divide than many would expect. Moreover, most a ccessibility guidelines address ing their needs just deal with captioning and audio-content transcriptio n. However, this approach to the problem does not consider that deaf people have big troubles with vocal languages, even in their written form. At present, only a few organizations, like W3C, produced guidelines deal ing with one of their most distinctive expressions: Sign Language (SL). SL is, in fact, the visual -gestural language used by many deaf people to communicate with each other. The present work aims at supporting e-learning user experience (e - LUX) for these speci fic users by enhancing the accessibility of content and container services. In particular, we propose preliminary solutions to tailor activities which can be more fruitful when performed in one's own " native" language, which for most deaf people, especially younger ones, is represen ted by national SL.

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to build $\pi$-YALLI, a corpus that will enable to carry out research on Nahuatl in order to develop Language Models (LM), whether dynamic or not, which will make it possible to in turn enable the development of Natural Language Processing (NLP) tools such as: a) a grapheme unifier, b) a word segmenter, c) a POS grammatical analyser, d) a content-based Automatic Text Summarization; and possibly, e) a translator translator (probabilistic or learning-based).

We propose leveraging cognitive science research on emotions and communication to improve language models for emotion analysis. First, we present the main emotion theories in psychology and cognitive science. Then, we introduce the main methods of emotion annotation in natural language processing and their connections to psychological theories. We also present the two main types of analyses of emotional communication in cognitive pragmatics. Finally, based on the cognitive science research presented, we propose directions for improving language models for emotion analysis. We suggest that these research efforts pave the way for constructing new annotation schemes and a possible benchmark for emotional understanding, considering different facets of human emotion and communication.

This article presents a review of quantum computing research works for Natural Language Processing (NLP). Their goal is to improve the performance of current models, and to provide a better representation of several linguistic phenomena, such as ambiguity and long range dependencies. Several families of approaches are presented, including symbolic diagrammatic approaches, and hybrid neural networks. These works show that experimental studies are already feasible, and open research perspectives on the conception of new models and their evaluation.

The article aims at identifying what, from a structural point of view, AI based automatic translators cannot fully capture. It focuses on the machine's mistakes, in order to try to explain its causes. The biblical story of Ca\"in and Abel has been chosen because of its rich interpretive and critical tradition, but also because of its semantic difficulty. The investigation begins with the observation, for the translation of this text, of the language pairs and interfaces offered by the best known machine translation services (Google Translate, DeepL). A typology of the most frequent translation errors is then established. Finally, contemporary translations are compared, in order to underline the unique contribution of each. In conclusion, the article suggests a revision of translation theory and, corArtificial Intelligence, Translation, Limitations, Interpretation, Comparison, Unicityelatively, a reformulation of its technology concerning cultural texts.

This paper presents the proof-of-concept of the first automatic voice assistant ever built in Wolof language, the main vehicular language spoken in Senegal. This voicebot is the result of a collaborative research project between Orange Innovation in France, Orange Senegal (aka Sonatel) and ADNCorp, a small IT company based in Dakar, Senegal. The purpose of the voicebot is to provide information to Orange customers about the Sargal loyalty program of Orange Senegal by using the most natural mean to communicate: speech. The voicebot receives in input the customer's oral request that is then processed by a SLU system to reply to the customer's request using audio recordings. The first results of this proof-of-concept are encouraging as we achieved 22\% of WER for the ASR task and 78\% of F1-score on the NLU task.

This paper introduces "Media of Langue," a novel dictionary visualizing Inter-lingual semantic network/space. Our proposed Inter-lingual semantic network/space is formed solely from the accumulation of translation practices between two or more language systems, in contrast to existing semantic networks/spaces that explicitly use "intra"-lingual relations. By visualizing this network/space for humans, an Inter-lingual dictionary can be realized that points to the semantic place of many words at once with a chain of mutual translation, which also contains the functions of existing dictionaries such as bilingual and synonym dictionaries. We implemented and published this interface as a web application, focusing on seven language pairs. In this paper, we first describe Inter-lingual semantic network/space with its basic features and the way to develop it from bilingual corpora, then details the design of "Media of Langue," with a quick analysis and illustrative examples of use cases. Our website is www.media-of-langue.org. A demonstration video is available at https://youtu.be/98lXuX4yjsU.

In the present paper, we argue that Terminological Knowledge Bases (TKB) are all the more useful for addressing various needs as they do not fulfill formal criteria. Moreover, they intend to clarify the terminology of a given domain by illustrating term uses in various contexts. Thus we designed a TKB structure including 3 linked features: terms, concepts and texts, that present the peculiar use of each term in the domain. Note that concepts are represented into frames whose non-formal description is standardized. Associated with this structure, we defined modeling criteria at the conceptual level. Finaly, we discuss the situation of TKB with regard to ontologies, and the use of TKB for the development of AI systems.