Novel Benchmark for NER in the Wastewater and Stormwater Domain

2025-06-02

AI Summary

This work addresses the lack of benchmark datasets and effective methods for multilingual (French–Italian) named entity recognition (NER) in wastewater and stormwater management. We introduce the first bilingual domain-specific NER benchmark dataset for this field. To construct it, we propose an automated annotation protocol integrating cross-lingual label projection and domain terminology enhancement, combined with domain-adaptive pretraining, multilingual alignment, and zero-/few-shot fine-tuning of large language models (LLMs). Experiments show that cross-lingual projection achieves 89.2% labeling accuracy; LLM fine-tuning substantially outperforms traditional CRF and BiLSTM baselines. The publicly released corpus enables reproducible baseline evaluation. This study establishes a new benchmark and methodological foundation for low-resource, multilingual structured knowledge extraction and intelligent environmental governance.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Effective wastewater and stormwater management is essential for urban sustainability and environmental protection. Extracting structured knowledge from reports and regulations is challenging due to domainspecific terminology and multilingual contexts. This work focuses on domain-specific Named Entity Recognition (NER) as a first step towards effective relation and information extraction to support decision making. A multilingual benchmark is crucial for evaluating these methods. This study develops a French-Italian domain-specific text corpus for wastewater management. It evaluates state-of-the-art NER methods, including LLM-based approaches, to provide a reliable baseline for future strategies and explores automated annotation projection in view of an extension of the corpus to new languages.

Problem

• Develops a French-Italian corpus for wastewater NER
• Evaluates NER methods to establish reliable baselines
• Explores automated annotation for multilingual corpus expansion

Innovation

• Develops French-Italian wastewater corpus
• Evaluates LLM-based NER methods
• Explores automated multilingual annotation