[Post-Doc 2019] Formal and statistical modeling of dialogue application to
pathological data

One postdoc position (1 year) in Natural Language Processing / Machine
Learning is open in the Semagramme <https://team.inria.fr/semagramme/> and
SyNaLP team at LORIA <http://www.loria.fr/en/> and
at ATILF . This position will be funded by the impact
projet OLKI
Language and Knowledge for Citizens) from Université de Lorraine

*Read the pdf of the call:* postdocOlkiCall


– Starting date: fall 2019
– Duration: 1 year
– Deadline for application: September 09th, 2109 (Interviews end of
– Location: Nancy
France (1h30 from Paris by train)
– Salary: around 2,000 euros per month net income
– Informal inquiries can be sent by email to Maxime Amblard (
Email) and Chloé Braud (Email).
– Application: CV, motivation letter, PhD evaluation, master TOR and
support letter(s)


NLP, Discourse and Dialogue, machine learning, logic, corpora, natural
language, pathology

Scientific project

Modeling interaction is a crucial step for Natural Language Processing
(NLP), which requires the development of automatic tools able to simulate
these exchanges. A typical example is chatbots and all the services based
on them. But Dialogue Models face two types of difficulties.

(I) The first issue concerns the availability of resources and models that
can analyze and process dialogues. Modelling dialogues is very hard, in
particular because conversations high- light particular uses such as the
relationship between questions and answers. Recent models show that this
issue is beyond semantics understanding, and even more so beyond discourse.
Another perspective is to use Machine Learning approaches in order to
identify dialogical relations and dialogical interactions. Generally
speaking discursive analysis aims at building a structure representing the
semantic links between sentences.

(II) The second is that dialogue models must be coordinated with pragmatic
inferences at a higher level. In this case, we can refer to linguistic
models of dialogue, or to models that capture conceptual links. In
particular, we propose to apply these approaches to the SLAM project
corpus. The SLAM project links thought and language disorders. This corpus
consists of transcriptions of interviews with schizophrenic patients.
Exchanges with these patients contain interactions that are difficult to
interpret semantically and interactively.




The post-doctoral fellow must have a PhD in Computational Linguistics/NLP,
Computer Science or related fields, with a good programming skills. He/She
must be fluent in English and have demonstrated its ability to publish at
the highest international level. Note that Knowledge of French is NOT a

Supervision of students is possible, if wanted.

Educational level:

Ph. D.

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