Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
XML PDF e-pub
PDF e-Pub

Section: Overall Objectives

Overall Objectives

The ALMAnaCH project-team (ALMAnaCH was created as an Inria team (“équipe”) on the 1st January, 2017 and as a project-team on the 1st July 2019.) brings together specialists of a pluri-disciplinary research domain at the interface between computer science, linguistics, statistics, and the humanities, namely that of natural language processing, computational linguistics and digital and computational humanities and social sciences.

Computational linguistics is an interdisciplinary field dealing with the computational modelling of natural language. Research in this field is driven both by the theoretical goal of understanding human language and by practical applications in Natural Language Processing (hereafter NLP) such as linguistic analysis (syntactic and semantic parsing, for instance), machine translation, information extraction and retrieval and human-computer dialogue. Computational linguistics and NLP, which date back at least to the early 1950s, are among the key sub-fields of Artificial Intelligence.

Digital Humanities and social sciences (hereafter DH) is an interdisciplinary field that uses computer science as a source of techniques and technologies, in particular NLP, for exploring research questions in social sciences and humanities. Computational Humanities and computational social sciences aim at improving the state of the art in both computer sciences (e.g. NLP) and social sciences and humanities, by involving computer science as a research field.

ALMAnaCH is a follow-up to the ALPAGE project-team, which came to an end in December 2016. ALPAGE was created in 2007 in collaboration with Paris-Diderot University and had the status of an UMR-I since 2009. This joint team involved computational linguists from Inria as well as computational linguists from Paris-Diderot University with a strong background in linguistics, and proved successful. However, the context has changed since then, with the recent emergence of digital humanities and, more importantly, of computational humanities. This presents both an opportunity and a challenge for Inria computational linguists, as it provides them with new types of data (on which their tools, resources and algorithms can be used, thereby leading to new results in human sciences), as well as with new and challenging research problems, which, if solved, provide new ways of studying human sciences.

The scientific positioning of ALMAnaCH therefore extends that of ALPAGE. We remain committed to developing state-of-the-art NLP software and resources that can be used by academics and in the industry, including recent approaches based on deep learning. At the same time we continue our work on language modelling in order to provide a better understanding of languages, an objective that is reinforced and addressed in the broader context of computational humanities. Finally, we remain dedicated to having an impact on the industrial world and more generally on society, via multiple types of collaboration with companies and other institutions (startup creation, industrial contracts, expertise, etc.).

One of the main challenges in computational linguistics is to model and to cope with language variation. Language varies with respect to domain and genre (news wires, scientific literature, poetry, oral transcripts...), sociolinguistic factors (age, background, education; variation attested for instance on social media), geographical factors (dialects) and other dimensions (disabilities, for instance). But language also constantly evolves at all time scales. Addressing this variability is still an open issue for NLP. Commonly used approaches, which often rely on supervised and semi-supervised machine learning methods, require very large amounts of annotated data. They still suffer from the high level of variability found for instance in user-generated content, non-contemporary texts, as well as in domain-specific documents (e.g. financial, legal).

ALMAnaCH tackles the challenge of language variation in two complementary directions, supported by a third, transverse research axis on language resources. These three research axes do not reflect an internal organisation of eparate teams. They are meant to structure our scientific agenda, and most members of the project-team are involved in two or all of them.

ALMAnaCH's research axes, themselves structured in sub-axis, are the following:

  1. Automatic Context-augmented Linguistic Analysis

    1. Processing of natural language at all levels: morphology, syntax, semantics

    2. Integrating context in NLP systems

    3. Information and knowledge extraction

  2. Computational Modelling of Linguistic Variation

    1. Theoretical and empirical synchronic linguistics

    2. Sociolinguistic variation

    3. Diachronic variation

    4. Accessibility-related variation

  3. Modelling and development of Language Resources

    1. Construction, management and automatic annotation of text corpora

    2. Development of lexical resources

    3. Development of annotated corpora