@inproceedings{maudslay-teufel-2022-homonymy,
title = "Homonymy Information for {E}nglish {W}ord{N}et",
author = "Maudslay, Rowan Hall and
Teufel, Simone",
editor = "Kernerman, Ilan and
Krek, Simon",
booktitle = "Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.gwll-1.13",
pages = "90--98",
abstract = "A widely acknowledged shortcoming of WordNet is that it lacks a distinction between word meanings which are systematically related (polysemy), and those which are coincidental (homonymy). Several previous works have attempted to fill this gap, by inferring this information using computational methods. We revisit this task, and exploit recent advances in language modelling to synthesise homonymy annotation for Princeton WordNet. Previous approaches treat the problem using clustering methods; by contrast, our method works by linking WordNet to the Oxford English Dictionary, which contains the information we need. To perform this alignment, we pair definitions based on their proximity in an embedding space produced by a Transformer model. Despite the simplicity of this approach, our best model attains an F1 of .97 on an evaluation set that we annotate. The outcome of our work is a high-quality homonymy annotation layer for Princeton WordNet, which we release.",
}
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<abstract>A widely acknowledged shortcoming of WordNet is that it lacks a distinction between word meanings which are systematically related (polysemy), and those which are coincidental (homonymy). Several previous works have attempted to fill this gap, by inferring this information using computational methods. We revisit this task, and exploit recent advances in language modelling to synthesise homonymy annotation for Princeton WordNet. Previous approaches treat the problem using clustering methods; by contrast, our method works by linking WordNet to the Oxford English Dictionary, which contains the information we need. To perform this alignment, we pair definitions based on their proximity in an embedding space produced by a Transformer model. Despite the simplicity of this approach, our best model attains an F1 of .97 on an evaluation set that we annotate. The outcome of our work is a high-quality homonymy annotation layer for Princeton WordNet, which we release.</abstract>
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%0 Conference Proceedings
%T Homonymy Information for English WordNet
%A Maudslay, Rowan Hall
%A Teufel, Simone
%Y Kernerman, Ilan
%Y Krek, Simon
%S Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F maudslay-teufel-2022-homonymy
%X A widely acknowledged shortcoming of WordNet is that it lacks a distinction between word meanings which are systematically related (polysemy), and those which are coincidental (homonymy). Several previous works have attempted to fill this gap, by inferring this information using computational methods. We revisit this task, and exploit recent advances in language modelling to synthesise homonymy annotation for Princeton WordNet. Previous approaches treat the problem using clustering methods; by contrast, our method works by linking WordNet to the Oxford English Dictionary, which contains the information we need. To perform this alignment, we pair definitions based on their proximity in an embedding space produced by a Transformer model. Despite the simplicity of this approach, our best model attains an F1 of .97 on an evaluation set that we annotate. The outcome of our work is a high-quality homonymy annotation layer for Princeton WordNet, which we release.
%U https://aclanthology.org/2022.gwll-1.13
%P 90-98
Markdown (Informal)
[Homonymy Information for English WordNet](https://aclanthology.org/2022.gwll-1.13) (Maudslay & Teufel, gwll 2022)
ACL
- Rowan Hall Maudslay and Simone Teufel. 2022. Homonymy Information for English WordNet. In Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference, pages 90–98, Marseille, France. European Language Resources Association.