TY - GEN
T1 - Automated ESG Report Analysis by Joint Entity and Relation Extraction
AU - Ehrhardt, Adrien
AU - Nguyen, Minh Tuan
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - The banking industry has lately been under pressure, notably from regulators and NGOs, to report various Environmental, Societal and Governance (ESG) metrics (e.g., the carbon footprint of loans). For years at Crédit Agricole, a specialized division examined ESG and Corporate Social Responsibility (CSR) reports to ensure, e.g., the bank’s commitment to de-fund coal activities, and companies with social or environmental issues. With both an intensification of the aforementioned exterior pressure, and of the number of companies making such reports publicly available, the tedious process of going through each report has become unsustainable. In this work, we present two adaptations of previously published models for joint entity and relation extraction. We train them on a private dataset consisting in ESG and CSR reports annotated internally at Crédit Agricole. We show that we are able to effectively detect entities such as coal activities and environmental or social issues, as well as relations between these entities, thus enabling the financial industry to quickly grasp the creditworthiness of clients and prospects w.r.t. ESG criteria. The resulting model is provided at https://github.com/adimajo/renard_joint.
AB - The banking industry has lately been under pressure, notably from regulators and NGOs, to report various Environmental, Societal and Governance (ESG) metrics (e.g., the carbon footprint of loans). For years at Crédit Agricole, a specialized division examined ESG and Corporate Social Responsibility (CSR) reports to ensure, e.g., the bank’s commitment to de-fund coal activities, and companies with social or environmental issues. With both an intensification of the aforementioned exterior pressure, and of the number of companies making such reports publicly available, the tedious process of going through each report has become unsustainable. In this work, we present two adaptations of previously published models for joint entity and relation extraction. We train them on a private dataset consisting in ESG and CSR reports annotated internally at Crédit Agricole. We show that we are able to effectively detect entities such as coal activities and environmental or social issues, as well as relations between these entities, thus enabling the financial industry to quickly grasp the creditworthiness of clients and prospects w.r.t. ESG criteria. The resulting model is provided at https://github.com/adimajo/renard_joint.
KW - NLP
KW - Named Entity Recognition
KW - Relation extraction
U2 - 10.1007/978-3-030-93733-1_23
DO - 10.1007/978-3-030-93733-1_23
M3 - Conference contribution
AN - SCOPUS:85126251657
SN - 9783030937324
T3 - Communications in Computer and Information Science
SP - 325
EP - 340
BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings
A2 - Kamp, Michael
A2 - Kamp, Michael
A2 - Koprinska, Irena
A2 - Bibal, Adrien
A2 - Bouadi, Tassadit
A2 - Frénay, Benoît
A2 - Galárraga, Luis
A2 - Oramas, José
A2 - Adilova, Linara
A2 - Krishnamurthy, Yamuna
A2 - Kang, Bo
A2 - Largeron, Christine
A2 - Lijffijt, Jefrey
A2 - Viard, Tiphaine
A2 - Welke, Pascal
A2 - Ruocco, Massimiliano
A2 - Aune, Erlend
A2 - Gallicchio, Claudio
A2 - Schiele, Gregor
A2 - Pernkopf, Franz
A2 - Blott, Michaela
A2 - Fröning, Holger
A2 - Schindler, Günther
A2 - Guidotti, Riccardo
A2 - Monreale, Anna
A2 - Rinzivillo, Salvatore
A2 - Biecek, Przemyslaw
A2 - Ntoutsi, Eirini
A2 - Pechenizkiy, Mykola
A2 - Rosenhahn, Bodo
A2 - Buckley, Christopher
A2 - Cialfi, Daniela
A2 - Lanillos, Pablo
A2 - Ramstead, Maxwell
A2 - Verbelen, Tim
A2 - Ferreira, Pedro M.
A2 - Andresini, Giuseppina
A2 - Malerba, Donato
A2 - Medeiros, Ibéria
A2 - Fournier-Viger, Philippe
A2 - Nawaz, M. Saqib
A2 - Ventura, Sebastian
A2 - Sun, Meng
A2 - Zhou, Min
A2 - Bitetta, Valerio
A2 - Bordino, Ilaria
A2 - Ferretti, Andrea
A2 - Gullo, Francesco
A2 - Ponti, Giovanni
A2 - Severini, Lorenzo
A2 - Ribeiro, Rita
A2 - Gama, João
A2 - Gavaldà, Ricard
A2 - Cooper, Lee
A2 - Ghazaleh, Naghmeh
A2 - Richiardi, Jonas
A2 - Roqueiro, Damian
A2 - Saldana Miranda, Diego
A2 - Sechidis, Konstantinos
A2 - Graça, Guilherme
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
Y2 - 13 September 2021 through 17 September 2021
ER -
