Post-Doctoral Research Visit F/M Post-Doctoral Research Visit F/M Information extraction in specialty-language domains CLEE
il y a 4 semaines
Contexte et atouts du poste
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.
Work can include direct collaboration with other academic or industrial partners of the department.
Mission confiée
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 annotationsSpecial care will be given to the transferability of the methods to other specialty domains, rather than developing approaching that are tailored to one particular domain.
Principales activités
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 annotationsSpecial care will be given to the transferability of the methods to other specialty domains, rather than developing approaching that are tailored to one particular domain.
Compétences
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. Knowledge of a second foreign language would be appreciated.
Avantages
Subsidized meals Partial reimbursement of public transport costs Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.) Possibility of teleworking and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage-
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