*Place of work*: Inria center in Paris area
(Paris / Rocquencourt / Saclay)
*Duration*: 2 years
*Starting date*: as soon as possible
*Keywords*: artificial intelligence, natural language processing,
information extraction, domain adaptation, chemistry, cybersecurity,
geopolitics


*Context*

This post-doc position fits within the roadmap activities of Inria's
Defense & Security Department, which is devoted to applications-driven
research.

Among the various fields of NLP, information extraction is a crossroad
topic that, by focusing on how to turn raw documents into structured
data models, echoes the practical needs of many end-users in a broad
range of sectors. Information extraction components such as entity
recognition or relation extraction are thus key to a number of
industrial and general-public applications.

However, whereas information extraction has seen major progresses in
the last few years on common language (Wikipedia, news, everyday
language), it still lags behind on specialty language, which
effectively affects a number of practical applications. Main challenges
include unknown words and concepts, unusual phrasings, or differences
in the nature of information that is interesting to extract.

The goal of this post-doc is to bridge that gap by developing new
methods that enable to model and account for the specificities of a
given domain with specialty language, while still benefitting from the
models and capabilities developed for the common language.

The first specialty-language domain that has been identified as a test
bed for the developed approaches is the scientific literature on
chemistry (e.g. ChemRxiv papers). Other domains that are considered for
experimentation throughout the period are cybersecurity (e.g. technical
documentation) and geopolitics. Inspiration can be drawn from existing
work on biomedical NLP, but that domain is not expected to be at the
core of the work.

The post-doc will work under the supervision of Lauriane Aufrant (lead
NLP researcher at Inria Defense & Security). Work can include direct
collaboration with other academic or industrial partners of the
department.


*Candidate profile*

-   Holding a PhD (or about to defend) in Natural Language Processing,
    Computational Linguistics or Computer Science with a specialization
    in Machine Learning
-   Theoretical and practical knowledge of deep learning, as well as
    traditional machine learning. Experience with knowledge-driven or
    hybrid AI would be appreciated.
-   Prior experience on at least one of the following topics:
    information extraction, semi-supervised learning, domain
    adaptation, low-resourced NLP
-   Strong programming skills (at least Python, git, Linux environment)
-   Fluency in English. Knowledge or interest for the French language.
    Knowle of a second foreign language would be appreciated.


*How to apply*

Send a CV and a cover letter to lauriane.aufrant and frederique.segond
(both at inria.fr)
Indications of referees or reference letters would be appreciated but
are not mandatory.


*Work description*

The post-doc will focus on developing new algorithmic methods along the
following research tracks:
-   Automated terminology and concepts extraction
-   Identification of new relations that are specific to a domain
-   Adapting models (in particular embedding models) to account for
    extended vocabulary
-   Semi-supervised learning to leverage a small amount of in-domain
    annotations

Special care will be given to the transferability of the methods to
other specialty domains, rather than developing approaches that are
tailored to one particular domain.