@inproceedings{thorne-akhondi-2024-nlp,
title = "{NLP} for Chemistry {--} Introduction and Recent Advances",
author = "Thorne, Camilo and
Akhondi, Saber",
editor = "Klinger, Roman and
Okazaki, Naozaki and
Calzolari, Nicoletta and
Kan, Min-Yen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-tutorials.8/",
pages = "45--49",
abstract = "In this half-day tutorial we will be giving an introductory overview to a number of recent applications of natural language processing to a relatively underrepresented application domain: chemistry. Specifically, we will see how neural language models (transformers) can be applied (oftentimes with near-human performance) to chemical text mining, reaction extraction, or more importantly computational chemistry (forward and backward synthesis of chemical compounds). At the same time, a number of gold standards for experimentation have been made available to the research {--}academic and otherwise{--} community. Theoretical results will be, whenever possible, supported by system demonstrations in the form of Jupyter notebooks. This tutorial targets an audience interested in bioinformatics and biomedical applications, but pre-supposes no advanced knowledge of either."
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%0 Conference Proceedings
%T NLP for Chemistry – Introduction and Recent Advances
%A Thorne, Camilo
%A Akhondi, Saber
%Y Klinger, Roman
%Y Okazaki, Naozaki
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F thorne-akhondi-2024-nlp
%X In this half-day tutorial we will be giving an introductory overview to a number of recent applications of natural language processing to a relatively underrepresented application domain: chemistry. Specifically, we will see how neural language models (transformers) can be applied (oftentimes with near-human performance) to chemical text mining, reaction extraction, or more importantly computational chemistry (forward and backward synthesis of chemical compounds). At the same time, a number of gold standards for experimentation have been made available to the research –academic and otherwise– community. Theoretical results will be, whenever possible, supported by system demonstrations in the form of Jupyter notebooks. This tutorial targets an audience interested in bioinformatics and biomedical applications, but pre-supposes no advanced knowledge of either.
%U https://aclanthology.org/2024.lrec-tutorials.8/
%P 45-49
Markdown (Informal)
[NLP for Chemistry – Introduction and Recent Advances](https://aclanthology.org/2024.lrec-tutorials.8/) (Thorne & Akhondi, LREC-COLING 2024)
ACL
- Camilo Thorne and Saber Akhondi. 2024. NLP for Chemistry – Introduction and Recent Advances. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries, pages 45–49, Torino, Italia. ELRA and ICCL.