| More Information: | Context
The NanoBubbles ERC Synergy project’s objective
(https://nanobubbles.hypotheses.org) is to understand how, when and why science
fails to correct itself. The project focuses on claims made within the field of
nanobiology. Project members combine approaches from the natural sciences,
computer science, and the social sciences and humanities (Science and Technology
Studies) to understand how error correction in science works and what obstacles
it faces. For this purpose, we aim to trace claims and corrections through
various channels of scientific communication (journals, social media,
advertisements, conference programs, etc.) via both qualitative and digital
methods.
Intership objectifs
Entity recognition is an important step for downstream treatment in natural
language processing. It consists in identifying the entities in a corpus
belonging to a specific domain and in their labeling. Training methods relying
on large annotated corpora are usually used for this purpose. However, such
resource are not always available for specific domains, and alternative methods
have to be employed (Hedderich 2020).
Distant supervision (Mintz 2009) is a technique used to automatically label
textual data using an external resource such as dictionaries (Shang 2018),
gazetteers, ontologies (Wang 2021) and knowledge bases (Sun 2019). This enable
the construction of a training corpus without the need of manual annotation. In
specialized domains, this is especially useful in order to annotate complex and
discontinuous entities with which human annotators may struggle (Khandelwal
2022).
The objective of this internship is to implement a method to automatically
annotate a corpus of scientific documents, using existing resources, in the
nanobiology domain. After it, they will employ existing deep learning approaches
(Liang 2020) to train an entity extraction model for entities in the nanobiology
domain.
Skills
• Being enrolled in a Master in Natural Language Processing, computer science
or data science.
• Good programming skills in Python, including experiences with natural
language processing tools
and methods, knowledge of machine learning and deep learning frameworks and
semantic web.
• Ability to communicate and write in English is a plus.
Scientific environment
The work will be conducted within the Sigma team of the LIG laboratory
(http://sigma.imag.fr [sigma.imag.fr]). The recruited person will be welcomed
within the team which offer a stimulating, multinational and pleasant working
environment.
Instructions for applying
Applications must contain a CV + letter/message of motivation + master grades +
letter(s) of recommendation (or names for potential letters), and be addressed
to Cyril Labbé (cyril.labbe@imag.fr) and Amira Barhoumi
(amira.barhoumi@univ-grenoble-alpes.fr). Applications will be considered on the
fly. It is therefore advisable to apply as soon as possible.
References
• Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009, August). Distant
supervision for relation extraction without labeled data. In Proceedings of the
Joint Conference of the 47th Annual Meeting of the ACL and the 4th International
Joint Conference on Natural Language Processing of the AFNLP (pp. 1003-1011).
• Shang, J., Liu, L., Ren, X., Gu, X., Ren, T., & Han, J. (2018). Learning
named entity tagger using domain-specific dictionary. arXiv preprint
arXiv:1809.03599.
• Sun, Y., & Loparo, K. (2019, July). Information extraction from free text in
clinical trials with knowledge-based distant supervision. In 2019 IEEE 43rd
Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, pp.
954-955). IEEE.
• Wang, X., Hu, V., Song, X., Garg, S., Xiao, J., & Han, J. (2021, November).
CHEMNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided
Distant Supervision. In Proceedings of the 2021 Conference on Empirical Methods
in Natural Language Processing (pp. 5227-5240).
• Liang, C., Yu, Y., Jiang, H., Er, S., Wang, R., Zhao, T., & Zhang, C. (2020,
August). Bond: Bert-assisted open-domain named entity recognition with distant
supervision. In Proceedings of the 26th ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining (pp. 1054-1064).
• Hedderich, M. A., Lange, L., Adel, H., Str ?otgen, J., & Klakow, D. (2020).
A survey on recent approaches for natural language processing in low-resource
scenarios. arXiv preprint arXiv:2010.12309.
• Khandelwal, A., Kar, A., Chikka, V. R., & Karlapalem, K. (2022, May).
Biomedical NER using Novel Schema and Distant Supervision. In Proceedings of the
21st Workshop on Biomedical Language Processing (pp. 155-160) |