Extracting domain-specific terms using contextual word embeddings

• URL: http://arxiv.org/abs/2502.17278v1
• Date: Mon, 24 Feb 2025 16:06:35 GMT
• Title: Extracting domain-specific terms using contextual word embeddings
• Authors: Andraž Repar, Nada Lavrač, Senja Pollak,
• Abstract summary: This paper proposes a novel machine learning approach to terminology extraction.<n>It combines features from traditional term extraction systems with novel contextual features derived from contextual word embeddings.<n>Our approach provides significant improvements in terms of F1 score over the previous state-of-the-art.
• Score: 2.7941582470640784
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Automated terminology extraction refers to the task of extracting meaningful terms from domain-specific texts. This paper proposes a novel machine learning approach to terminology extraction, which combines features from traditional term extraction systems with novel contextual features derived from contextual word embeddings. Instead of using a predefined list of part-of-speech patterns, we first analyse a new term-annotated corpus RSDO5 for the Slovenian language and devise a set of rules for term candidate selection and then generate statistical, linguistic and context-based features. We use a support-vector machine algorithm to train a classification model, evaluate it on the four domains (biomechanics, linguistics, chemistry, veterinary) of the RSDO5 corpus and compare the results with state-of-art term extraction approaches for the Slovenian language. Our approach provides significant improvements in terms of F1 score over the previous state-of-the-art, which proves that contextual word embeddings are valuable for improving term extraction.

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