# Using contextual sentence analysis models to recognize ESG concepts

arXiv: https://arxiv.org/abs/2207.01402 · HTML: https://mlsanity.com/paper/2207.01402/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Commonsense Knowledge Base Completion with Structural and Semantic Context](https://mlsanity.com/paper/1910.02915/) (2019)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (2020)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [Predicting the Semantic Textual Similarity with Siamese CNN and LSTM](https://mlsanity.com/paper/1810.10641/) (2018)
- [A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts](https://mlsanity.com/paper/2101.10196/) (2021)

## Built on by (most cited first, 5 of 5)

- [When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP](https://mlsanity.com/paper/2305.14007/) (2023)
- [Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification](https://mlsanity.com/paper/2309.02189/) (2023)
- [Enhancing ESG Impact Type Identification through Early Fusion and Multilingual Models](https://mlsanity.com/paper/2402.10772/) (2024)
- [Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone](https://mlsanity.com/paper/2405.17170/) (2024)
- [Improving Clustering on Occupational Text Data through Dimensionality Reduction](https://mlsanity.com/paper/2507.07582/) (2025)

## Abstract (arXiv)

This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the Shared Task FinSim-4 evaluation campaign. The first sub-task aims to enrich the 'Fortia ESG taxonomy' with new lexicon entries while the second one aims to classify sentences to either 'sustainable' or 'unsustainable' with respect to ESG (Environment, Social and Governance) related factors. For the first sub-task, we proposed a model based on pre-trained Sentence-BERT models to project sentences and concepts in a common space in order to better represent ESG concepts. The official task results show that our system yields a significant performance improvement compared to the baseline and outperforms all other submissions on the first sub-task. For the second sub-task, we combine the RoBERTa model with a feed-forward multi-layer perceptron in order to extract the context of sentences and classify them. Our model achieved high accuracy scores (over 92%) and was ranked among the top 5 systems.