@inproceedings{hatty-etal-2020-predicting,
title = "Predicting Degrees of Technicality in Automatic Terminology Extraction",
author = {H{\"a}tty, Anna and
Schlechtweg, Dominik and
Dorna, Michael and
Schulte im Walde, Sabine},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.258",
doi = "10.18653/v1/2020.acl-main.258",
pages = "2883--2889",
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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<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.</abstract>
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%0 Conference Proceedings
%T Predicting Degrees of Technicality in Automatic Terminology Extraction
%A Hätty, Anna
%A Schlechtweg, Dominik
%A Dorna, Michael
%A Schulte im Walde, Sabine
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F hatty-etal-2020-predicting
%X 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.
%R 10.18653/v1/2020.acl-main.258
%U https://aclanthology.org/2020.acl-main.258
%U https://doi.org/10.18653/v1/2020.acl-main.258
%P 2883-2889
Markdown (Informal)
[Predicting Degrees of Technicality in Automatic Terminology Extraction](https://aclanthology.org/2020.acl-main.258) (Hätty et al., ACL 2020)
ACL