A Clique-based Graphical Approach to Detect Interpretable Adjectival Senses in Hungarian

Enikő Héja, Noémi Ligeti-Nagy


Abstract
The present paper introduces an ongoing research which aims to detect interpretable adjectival senses from monolingual corpora applying an unsupervised WSI approach. According to our expectations the findings of our investigation are going to contribute to the work of lexicographers, linguists and also facilitate the creation of benchmarks with semantic information for the NLP community. For doing so, we set up four criteria to distinguish between senses. We experiment with a graphical approach to model our criteria and then perform a detailed, linguistically motivated manual evaluation of the results.
Anthology ID:
2022.textgraphs-1.4
Volume:
Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Dmitry Ustalov, Yanjun Gao, Alexander Panchenko, Marco Valentino, Mokanarangan Thayaparan, Thien Huu Nguyen, Gerald Penn, Arti Ramesh, Abhik Jana
Venue:
TextGraphs
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–43
Language:
URL:
https://aclanthology.org/2022.textgraphs-1.4
DOI:
Bibkey:
Cite (ACL):
Enikő Héja and Noémi Ligeti-Nagy. 2022. A Clique-based Graphical Approach to Detect Interpretable Adjectival Senses in Hungarian. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 35–43, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
A Clique-based Graphical Approach to Detect Interpretable Adjectival Senses in Hungarian (Héja & Ligeti-Nagy, TextGraphs 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.textgraphs-1.4.pdf
Code
 nytud/huwic