# ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents

Published:

Jiayuan He, Dat Quoc Nguyen, Saber A. Akhondi, Christian Druckenbrodt, Camilo Thorne, Ralph Hoessel, Zubair Afzal, Zenan Zhai, Biaoyan Fang, Hiyori Yoshikawa, Ameer Albahem, Lawrence Cavedon, Trevor Cohn, Timothy Baldwin and Karin Verspoor (2021) ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents. Frontiers in Research Metrics and Analytics 6

@ARTICLE{10.3389/frma.2021.654438,

AUTHOR={He, Jiayuan and Nguyen, Dat Quoc and Akhondi, Saber A. and Druckenbrodt, Christian and Thorne, Camilo and Hoessel, Ralph and Afzal, Zubair and Zhai, Zenan and Fang, Biaoyan and Yoshikawa, Hiyori and Albahem, Ameer and Cavedon, Lawrence and Cohn, Trevor and Baldwin, Timothy and Verspoor, Karin},

TITLE={ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents},

JOURNAL={Frontiers in Research Metrics and Analytics},

VOLUME={6},

PAGES={12},

YEAR={2021},

URL={https://www.frontiersin.org/article/10.3389/frma.2021.654438},

DOI={10.3389/frma.2021.654438},

ISSN={2504-0537},

ABSTRACT={Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1) chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2) event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.}
}


## Abstract

Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1) chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2) event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.