Research projects

Transforming archival research on the Holocaust, by providing methods and tools to integrate and provide access to a wide variety of archival content.

Coordinating Digital Humanities infrastructure activities in Europe (17 partners, 5 associated partners).

In order to support the fight against radicalization and thus prevent future terrorist attacks from taking place, the CounteR project brings data from disperse sources into an analysis and early alert platform for data mining and prediction of critical areas (e.g. communities), aiming to be a frontline community policing tool which looks at the community and its related risk factors rather than targeting and surveilling individuals. The system will incorporate state of the art NLP technologies combined with expert knowledge into the psychology of radicalization processes to provide a complete solution for law enforcement authorities to understand the when, where and why of radicalization in the community.

Context-aware parsing and machine translation of user-generated content.

Digitalisation and computational annotation and exploitation of Henri Basnage de Beauval’s encyclopedic dictionary (1701).

This project aims at building a cultural history of Artificial Intelligence, based on a mixed method, combining the methods of the history of ideas and the history of collective imaginations with a search of scientific literature and ethnographic field work among AI creators.

The PRAIRIE Institute (PaRis AI Research InstitutE) is one of the four French Institutes of Artificial Intelligence, which were created as part of the national French initiative on AI announced by President Emmanuel Macron on May 29, 2018. PRAIRIE’s objective is to become within five years a world leader in AI research and higher education, with an undeniable impact on economy and technology at the French, European and global levels. It brings together academic members (“PRAIRIE chairs”) who excel at research and education in both the core methodological areas and the interdisciplinary aspects of AI, and industrial members that are major actors in AI at the global level and a very strong group of international partners.

The text simplification algorithm developed within Cap'FALC is based on neural models for natural language processing. It will work similarly to a spell checker, which marks passages in a text, offers solutions but does not correct without a human validation step. The tool is intended to represent a valuable aid for disabled people responsible for transcribing texts in FALC, not to replace their intervention at all stages of the drafting; only their expertise can validate a text as being accessible and easy to read and understand.

Digitalisation and computational exploitation of archives of historical interest.

Empirical foundations of linguistics, including computational linguistics and natural language processing. ALMAnaCH’s predecessor team ALPAGE was one of the partner teams of this LabEx, which gathers a dozen of teams within and around Paris whose research interests include one aspects of linguistics or more. Several ALMAnaCH members are now “individual members” of the LabEx EFL. B. Sagot serves as deputy head (and former head) of one of the scientific strands of the LabEx, namely strand 6 dedicated to language resources. Benoît Sagot and D; Seddah are (co-)heads of a number of scientific “operations” within strands 6, 5 (“computational semantic analysis”) and 2 (“experimental grammar”). Main collaborations are related to language resource development (strands 5 and 6), syntactic and semantic parsing (strand 5, especially with LIPN [CNRS and U. Paris 13]) and computational morphology (strands 2 and 6, especially with CRLAO [CNRS and Inalco] and LLF [CNRS and Paris-Diderot]).

ALMAnaCH is a member of the CORLI consortium on “corpora, languages and interactions” (B. Sagot is a member of the consortium’s board).

The DIM « Matériaux anciens et patrimoniaux » (MAP) is a region-wide research network. Its singularity relies on a close collaboration between human sciences, experimental sciences such as physics and chemistry, scientific ecology and information sciences, while integrating socio-economical partners from the cultural heritage environment. Based on its research, development and valorization potential, we expect such an interdisciplinary network to raise the Ile-de-France region up to a world-top position as far as heritage sciences and research on ancient materials are concerned.

The project is intended to digitise and exploit the early editions (beginning of the 20th century) of the Petit Larousse dictionary. ALMAnaCH is involved in the automatic extraction of the dictionary content by means of the GROBID-dictionary and in defining a TEI-compliant interchange format for all results.

The project aims at contributing to the proper transition between a basic digitalisation of cultural heritage content and the actual usage of the corresponding content within a "collection as data" perspective. To acheive this, we experiment news methods for extracting the logical structure of scanned (and OCRed) catalogues and standardise their content for publication towards curators, researchers, or wider users.

The Universal Dependencies project is an open community effort with over 300 contributors producing nearly 200 treebanks in over 100 languages. Universal Dependencies is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages. With a release every 6 months since 2017, the UD framework is the de-facto standard for syntactic structures representations and of course the basis for most if not all supervised neural parsers.

This collaboration aims at fostering discussions and reflections around the research questions surrounding large language models (capabilities, limitations, potential improvements, bias, ethics, environmental impact, role in the general AI/cognitive research landscape) as well as the challenges around creating and sharing such models and datasets for research purposes and among the research community. The collaborative tasks involves creating, sharing and evaluating a large multilingual dataset and a large multilingual generative language model. An uncommonly large compute budget was allocated for these collaborative tasks (several millions GPU hours on several thousands GPUs, in particular on the French public cluster Jean Zay).