PhD in NLP - PATRIMALP Materials, Pigments, Lights: the colors of Heritage – Natural Language Processing for cultural heritage
Starting date: October 01, 2022 (flexible) Application deadline: September 5th, 2022 Interviews (tentative): September 12th, 2022 Salary: 1 975 € gross/month (social security included) Mission: research oriented (teaching possible but not mandatory)
Keywords: natural language processing, knowledge representation, cultural heritage, transfer learning, multilingualism
CONTEXT
The main challenge of the Patrimalp project is the development of an integrated and interdisciplinary Heritage Science, in order to ensure cultural Heritage sustainability, promotion and dissemination in contemporary society. The ambition is to produce the forms of intelligibility of a global and moving process which starts from the collection of the raw material, its transformation into a primitive object, different lives as a material (alterations, degradations, transformations ...) and as a symbol (relegation, disinterest, oblivion or rebirth, exaltation...) throughout history, and finally from its election as an object of historical and Heritage value and its “promotion” into a work of art. This research is applied to understand how inks and pigments have been conceived over several centuries, how it has been used in art work as well as how the handcrafting method has evolved and been disseminated over centuries and countries.
To make this study possible, the project will gather a large collection of textual material made up of alchemical works and collections of natural or artificial objects collected between the 16th and 18th centuries. To better understand the choice of colors for these "wonders", we want to reconstruct the recipes for making colored material in its context of thought, whether technical or symbolic. These recipes will constitute a new body of research for literary people and a new data-study case for building knowledge about color. This corpus indeed offers modes of representation inscribed in complex forms of writing and fiction whose modalities and frames of reference remain to be analyzed (accounts of technical, medical or physico-chemical experiments inscribed in fictional worlds or mythological, symbolic descriptions of artifacts, or materials collected in nature, mines). On the linguistic level, the inventory of this lexicon in different European and Eastern languages will lead to the formalization of the knowledge of these various skills over time and several cultures. This corpus will thus provide complex data on the material and symbolic origin of the ingredients of color, on its use, its names and its physical or symbolic perception: these data represent a challenge for computer researchers who will have to organize them in order to benefit curators, chemists or physicists, in ontologies representing the state of knowledge from the point of view of scholars over the ages. To systematically explore the corpus of these recipes, we will use NLP techniques to uncover the correlations between recipes, physical and chemical composition of objects and symbolic references. The final objective is to build a knowledge base (objects, components of objects, materials, colors, know-how, reference framework) each of the parts being able to reference a specific ontology (ontology of pigments, materials, colors...) to make it possible for researchers to observe the trajectory from the writing of color to its technical and artisan practice from this specific corpus.
PHD OBJECTIVES
The PhD project will focus on segmenting, extracting and representing recipes from a corpus of alchemical works from the 16th and 18th centuries to make them accessible to researchers in the humanities. This necessitates to : · identify which excerpts of the text belong to a recipe; · supervise an annotation campaign to build an analysis and training corpus · build NLP tools to extract automatically the list of elements (raw material, tools, quantity, units) and actions (verb, adverb, adjective) that made up the recipes; · analyze the dependencies between the elements of a recipe rules ; · Represent these rules in a formal knowledge representation.
The results of this processing will support : · The documentation of this unique set of text, by inserting the extracted elements to the document meta data to easy retrieval · The building a knowledge base of alchemical recipes This PhD will need to address several challenges. One of them is to be able to process text composed of multiple non-modern languages (French, German, English, Latin, Greek) [Coavoux2022,Grobol2022] . One approach we will be to study how large multilingual pre-trained models [Delvin2019, Xue2020] can be leveraged and adapted for the task and how disparate collection of corpora of ancient texts can be used to fine-tune them. Another challenge will be the paucity of data for the downstream tasks (segmentation, parsing, Natural Language Understanding [Desot2022]) for this we will need to identify other related corpus (e.g. cooking) to address the problem in a multitask setting (such as NER and NLU) and transfer learning.
SKILLS · Master 2 in Natural Language Processing, computer science or data science. · Good mastering of Python programming and deep learning frameworks. · Previous experience in text classification, parsing, processing of several languages or text retrieval would be a plus · Very good communication skills in English and good command of French
SCIENTIFIC ENVIRONMENT
The thesis will be conducted within the STEAMER and GETALP teams of the LIG laboratory (http://steamer.imag.fr/%C2%A0and%C2%A0https://lig-getalp.imag.fr/).The GETALP team has strong expertise and track record in Natural Language Processing, STEAMER team has strong expertise in Knowkledge representation and reasoning.The recruited person will be welcomed within the teams which offer a stimulating, multinational and pleasant working environment. The means to carry out the PhD will be provided both in terms of missions in France and abroad and in terms of equipment (personal computer, access to the LIG GPU servers). The PhD candidate will collaborate with the partners involved in the PATRIMALP project, in particular with Laurence Rivière from the LUHCIE lab (Laboratoire Universitaire Histoire Cultures Italie Europe) and Véronique Adam from the LITT&ARTS lab (Littératures et Arts).
INSTRUCTIONS FOR APPLYING
Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Danielle Ziebelin (Danielle.Ziebelin@univ-grenoble-alpes.fr), François Portet (francois.Portet@imag.fr) Maximin Coavoux (Maximin.Coavoux@univ-grenoble-alpes.fr)
REFERENCES
[Coavoux2022] Maximin Coavoux, Corinne Denoyelle, Olivier Kraif, Julie Sorba. Phraséologie du roman médiéval en prose. Diachro X – le français en diachronie, Sorbonne Université, May 2022, Paris, France [Delvin2019] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL. [Desot 2022] Desot, T., Portet, F., & Vacher, M. (2022). End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting. Computer Speech & Language, 75, 101369. [Grobol2022] Loïc Grobol, Mathilde Regnault, Pedro Ortiz Suarez, Benoît Sagot, Laurent Romary and Benoit Crabbé BERTrade: Using Contextual Embeddings to Parse Old French. 13th International Conference on Language Resources and Evaluation (LREC 2022), May 2022, Marseille, France [Xue2020] Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., ... & Raffel, C. (2020). mT5: A massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934.