Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs

Eleri Aedmaa, Maximilian Köper, Sabine Schulte im Walde


Abstract
This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i.e., Estonian. We demonstrate the value of language-specific indicators induced from theoretical linguistic research, which outperform a high majority baseline when combined with language-independent features of non-literal language (such as abstractness).
Anthology ID:
N18-4002
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2018
Address:
New Orleans, Louisiana, USA
Editors:
Silvio Ricardo Cordeiro, Shereen Oraby, Umashanthi Pavalanathan, Kyeongmin Rim
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9–16
Language:
URL:
https://aclanthology.org/N18-4002
DOI:
10.18653/v1/N18-4002
Bibkey:
Cite (ACL):
Eleri Aedmaa, Maximilian Köper, and Sabine Schulte im Walde. 2018. Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 9–16, New Orleans, Louisiana, USA. Association for Computational Linguistics.
Cite (Informal):
Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs (Aedmaa et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-4002.pdf