Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks

Anna Hätty, Sabine Schulte im Walde


Abstract
Automatic term identification and investigating the understandability of terms in a specialized domain are often treated as two separate lines of research. We propose a combined approach for this matter, by defining fine-grained classes of termhood and framing a classification task. The classes reflect tiers of a term’s association to a domain. The new setup is applied to German closed compounds as term candidates in the domain of cooking. For the prediction of the classes, we compare several neural network architectures and also take salient information about the compounds’ components into account. We show that applying a similar class distinction to the compounds’ components and propagating this information within the network improves the compound class prediction results.
Anthology ID:
W18-4909
Volume:
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Agata Savary, Carlos Ramisch, Jena D. Hwang, Nathan Schneider, Melanie Andresen, Sameer Pradhan, Miriam R. L. Petruck
Venues:
LAW | MWE
SIGs:
SIGLEX | SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
62–73
Language:
URL:
https://aclanthology.org/W18-4909
DOI:
Bibkey:
Cite (ACL):
Anna Hätty and Sabine Schulte im Walde. 2018. Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 62–73, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks (Hätty & Schulte im Walde, LAW-MWE 2018)
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PDF:
https://aclanthology.org/W18-4909.pdf