A computational framework for environmental corpus construction in business English interpretation based on deep learning

Abstract

In the context of sustainable development, the effective construction of environmental corpus for business English interpretation is crucial for promoting the standardization of professional language services. Traditional corpus construction relies heavily on manual collection and annotation, which is characterized by low efficiency and high labor costs. To address these issues, this study proposes a computational framework integrating web crawler technology and deep learning-based annotation. For corpus collection, a targeted crawler algorithm is designed to automatically extract and preprocess sustainable development-related business English texts from multi-source platforms, achieving a data coverage rate of 92.3% for key environmental business domains. For annotation, an Attention-LSTM hybrid model is constructed to realize semi-automatic labeling of professional terms and contextual relationships. The model is trained on a manually annotated sample set, and experimental results show that its annotation accuracy reaches 89.7%, which is 18.5% higher than that of traditional rule-based methods, and the manual correction workload is reduced by 63.2%. This framework not only improves the efficiency of environmental corpus construction in business English interpretation but also provides a computational solution for domain-specific corpus engineering, laying a technical foundation for intelligent language service systems in sustainable development scenarios.