Using BERT-CRF to Build an Automatic English Terminology Annotation Engine for Higher Vocational Courses
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Abstract
In higher vocational education, professional English terminology emphasizes more precise semantic expression in specific professional fields than general academic English. This demand for terminological precision is critical within technical disciplines where compound and nested terms carry strict professional meanings. Existing annotation methods, relying on general corpus training, struggle to accurately handle the compound and nested terminology common in higher vocational courses. To address this, this paper introduces the TermTag-BERTCRF model, an English term automatic tagging engine. The model enhances performance by pre-training BERT on a specific vocational corpus and using a BiLSTM-CRF structure to optimize the recognition of compound and nested term boundaries. It integrates multi-level manual features and employs a dual-dimensional labeling system for multi-granularity annotation. The deployed lightweight model offers efficient batch processing and real-time labeling via API and visualization. Experiments show the TermTag-BERTCRF model outperforms the baseline, achieving an F1 score of 91.0% overall, and high accuracy for complex 3-word (85.4%) and 4-word (78.7%) terms. With fast response (128.6 ms) and strong stability under noise, this model provides effective and promising support for the automatic annotation and knowledge extraction of English terms in higher vocational education. This technology can benefit industry-specific training by precisely identifying and extracting complex technical terminology, thereby improving the efficiency of creating accurate domain-specific educational resources. The same annotation logic can support terminology organization in courses involving antennas, waveguides and impedance matching.
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