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Beyond Dictionaries: Machine-Learned Normalization of Emission Units in Information Extraction

MCML Authors

Abstract

Sustainability reports denote greenhouse gas emissions with heterogeneous unit strings, such as abbreviations, inconsistent formatting, and scale markers. In emission extraction pipelines, these units must be normalized to a common target, e.g. t CO2e, to enable reliable comparison and aggregation of emissions across companies, reporting years, scopes, and regions. We propose a hybrid normalization approach: If a raw unit string is not found in a curated dictionary, a machine learning classifier maps noisy unit strings to normalized emission-unit categories that can then be converted to the target unit. The classifier is trained using character-level TF-IDF, lightweight string features, and LLM-assisted data augmentation. On random-splits of the augmented dataset, an ensemble model achieves approximately 95% accuracy. Additional evaluation on rare real-world unit strings indicates that supervised fallback classifiers can handle out-of-dictionary variants in climate-related information extraction.

inproceedings ROS+26

ClimateNLP @EMNLP 2026

Tutorials at the Conference on Empirical Methods in Natural Language Processing. Budapest, Hungary, Oct 24-29, 2026.

Authors

L. Reichenbach • S. Ohler • M. Schierholz • A. Steinberg Schulten

Links

DOI

Research Area

 C4 | Computational Social Sciences

BibTeXKey: ROS+26

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