Peacemaker at ATE-IT: Automatic term extraction from Italian text for waste management data using encoder model

2026-05-31

AI Summary

This study addresses the challenges of scarce annotated data and difficulties in multi-word term extraction under domain shift in Italian waste management texts. To tackle these issues, the authors propose a low-cost, interpretable approach to automatic term extraction based on a fine-tuned encoder-based pretrained language model. The method achieves efficient term identification under limited computational resources and is evaluated using both type-level and token-level metrics. When applied to Task A of the ATE shared task (ATE-IT), the approach yields stable and balanced performance in precision, recall, and F1 score, comparable to that of other participating teams. These results demonstrate its effectiveness and offer a reliable, scalable solution for term extraction in low-resource scenarios.

Abstract

The development of automatic term extraction has become increasingly important in modern technology. Automatic term extraction can be found in virtually every search engine that is currently available to users. Recent advancements have provided promising results for the extraction of automatic terms; however, accurate labeling is difficult because of several factors, such as the limited number of annotated documents available for training and the complexity of extracting multi-word expressions due to shifts in the domain. In this paper, we will present a low-cost and interpretable method of automatic term extraction, developed specifically for Task A of the ATE Shared Task. This new method utilizes fine-tuning extraction strategies that can run on a small amount of computational resources. We evaluated our automated system using both type-level and micro-level measures of precision, recall, and F1-score to measure both complementary aspects of the extraction performance. According to the experimental results, our proposed approach achieves consistent and balanced performance compared to other teams. Even though the technique itself is relatively straightforward, it serves as a good starting point for low-resource models. Overall, the findings point toward the possibility of significant future advancements (in model expansion) with higher-level performance still able to retain their ability to be interpreted.

Problem

• automatic term extraction
• low-resource
• multi-word expressions
• domain-specific terminology
• Italian text

Innovation

• automatic term extraction
• low-resource
• encoder model
• interpretability
• multi-word expressions