BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs

Ádám Kovács, Kinga Gémes, Andras Kornai, Gábor Recski

Abstract
In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task “Predicting Multilingual and Cross-lingual (graded) Lexical Entailment” (Glavaš et al., 2020). A detailed error analysis of our output uncovers future di- rections for improving both the semantic parsing method and the inference process on semantic graphs.
Anthology ID:
2020.semeval-1.15
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
135–141
Language:
URL:
https://aclanthology.org/2020.semeval-1.15
DOI:
10.18653/v1/2020.semeval-1.15
Bibkey:
Cite (ACL):
Ádám Kovács, Kinga Gémes, Andras Kornai, and Gábor Recski. 2020. BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 135–141, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs (Kovács et al., SemEval 2020)
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PDF:
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