Post-doc: Analysis of dialogues in psychosis or severe mental illness

One postdoc position (1 year) is open in the Semagramme team at LORIA and at ATILF. The position will be in Natural Language Processing / Machine Learning or Psychology and linguistic.

It will be funded by the project OLKi (Open Language and Knowledge for Citizens) from Université de Lorraine.


— Starting date : fall 2020

— Duration : 1 year

— The call is open while the position is available

— Location : Nancy 1, France

— Salary : around 2,000 euros per month net income

— Application : CV, motivation letter, PhD evaluation, master TOR and support letter(s) to


— Maxime Amblard, MCF HDR Univ. de Lorraine, Loria UMR 7503 – Team Sémagramme,

— Chloé Braud, CR CNRS, Irit UMR 5505 – Team Melodi,

— Michel Musiol, Pr Univ. de Lorraine, ATILF UMR 7118 – Team Discours,

Scientific environment

The project is part of an interdisciplinary work. It is integrated into the INRIA Exploratory Action ODiM project on the formal modeling of interviews of patients with schizophrenia. ODiM is an interdisciplinary project, at the interface of psychiatry-psychopathology, linguistics, formal semantics and digital sciences. It tends to replace the paradigm of Language and Thought Disorders (LTD) as used in the Mental Health sector with a semantic-formal and cognitive model of Discourse Disorders (DD). These disorders are translated into pathognomonic signs, making them complementary diagnostic tools as well as screening for vulnerable people before the psychosis’s trigger.
The project has three main components.
The work is based on real data from interviews with patients with schizophrenia. A data collection phase in partner hospitals and with a control group, consisting of interviews and neuropsychological and cognitive tests, is therefore necessary.
The data collection will allow the development of the theoretical model, both in psycholinguistic and semantic-formal formalization for the identification of diagnostic signs. The success of such a project requires the extension of the analysis methodology in order to increase the model’s ability to identify sequences with symptomatic discontinuities.
If the general objective of the project is to propose a methodological framework for defining and understanding diagnostic clues associated with psychosis, we also wish to equip these approaches by developing software to automatically identify these clues, both in terms of discourse and language behaviour.


NLP, Discourse and Dialogue, Machine Learning, Logic, Corpora, Natural Language, Pathology, Schizophrenia, Psychology


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