CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature

TL;DR

This paper presents CoastTerm, a specialized corpus of coastal scientific literature, and demonstrates effective automatic extraction and classification of domain-specific terms using transformer models, advancing coastal environmental research tools.

Contribution

Introduces a new corpus and methodology for automatic term extraction and classification in coastal science literature, leveraging transformer models for multidisciplinary applications.

Findings

Achieved approximately 80% F1 score for term extraction.

Achieved 70% F1 score for term classification.

Demonstrated potential for building a coastal knowledge base.

Abstract

The growing impact of climate change on coastal areas, particularly active but fragile regions, necessitates collaboration among diverse stakeholders and disciplines to formulate effective environmental protection policies. We introduce a novel specialized corpus comprising 2,491 sentences from 410 scientific abstracts concerning coastal areas, for the Automatic Term Extraction (ATE) and Classification (ATC) tasks. Inspired by the ARDI framework, focused on the identification of Actors, Resources, Dynamics and Interactions, we automatically extract domain terms and their distinct roles in the functioning of coastal systems by leveraging monolingual and multilingual transformer models. The evaluation demonstrates consistent results, achieving an F1 score of approximately 80\% for automated term extraction and F1 of 70\% for extracting terms and their labels. These findings are promising and signify an initial step towards the development of a specialized Knowledge Base dedicated to coastal areas.