Predicting Degrees of Technicality in Automatic Terminology Extraction

Anna Hätty, Dominik Schlechtweg, Michael Dorna, Sabine Schulte im Walde

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 2883–2889, 2020.

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Abstract

While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on technicality prediction. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general- vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.

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