@inproceedings{walentynowicz-piasecki-2023-wordnet,
title = "{W}ordnet-oriented recognition of derivational relations",
author = "Walentynowicz, Wiktor and
Piasecki, Maciej",
editor = "Rigau, German and
Bond, Francis and
Rademaker, Alexandre",
booktitle = "Proceedings of the 12th Global Wordnet Conference",
month = jan,
year = "2023",
address = "University of the Basque Country, Donostia - San Sebastian, Basque Country",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2023.gwc-1.39",
pages = "325--330",
abstract = "Derivational relations are an important element in defining meanings, as they help to explore word-formation schemes and predict senses of derivates (derived words). In this work, we analyse different methods of representing derivational forms obtained from WordNet {--} from quantitative vectors to contextual learned embedding methods {--} and compare ways of classifying the derivational relations occurring between them. Our research focuses on the explainability of the obtained representations and results. The data source for our research is plWordNet, which is the wordnet of the Polish language and includes a rich set of derivation examples.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="walentynowicz-piasecki-2023-wordnet">
<titleInfo>
<title>Wordnet-oriented recognition of derivational relations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wiktor</namePart>
<namePart type="family">Walentynowicz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maciej</namePart>
<namePart type="family">Piasecki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 12th Global Wordnet Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">German</namePart>
<namePart type="family">Rigau</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Francis</namePart>
<namePart type="family">Bond</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexandre</namePart>
<namePart type="family">Rademaker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Global Wordnet Association</publisher>
<place>
<placeTerm type="text">University of the Basque Country, Donostia - San Sebastian, Basque Country</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Derivational relations are an important element in defining meanings, as they help to explore word-formation schemes and predict senses of derivates (derived words). In this work, we analyse different methods of representing derivational forms obtained from WordNet – from quantitative vectors to contextual learned embedding methods – and compare ways of classifying the derivational relations occurring between them. Our research focuses on the explainability of the obtained representations and results. The data source for our research is plWordNet, which is the wordnet of the Polish language and includes a rich set of derivation examples.</abstract>
<identifier type="citekey">walentynowicz-piasecki-2023-wordnet</identifier>
<location>
<url>https://aclanthology.org/2023.gwc-1.39</url>
</location>
<part>
<date>2023-01</date>
<extent unit="page">
<start>325</start>
<end>330</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Wordnet-oriented recognition of derivational relations
%A Walentynowicz, Wiktor
%A Piasecki, Maciej
%Y Rigau, German
%Y Bond, Francis
%Y Rademaker, Alexandre
%S Proceedings of the 12th Global Wordnet Conference
%D 2023
%8 January
%I Global Wordnet Association
%C University of the Basque Country, Donostia - San Sebastian, Basque Country
%F walentynowicz-piasecki-2023-wordnet
%X Derivational relations are an important element in defining meanings, as they help to explore word-formation schemes and predict senses of derivates (derived words). In this work, we analyse different methods of representing derivational forms obtained from WordNet – from quantitative vectors to contextual learned embedding methods – and compare ways of classifying the derivational relations occurring between them. Our research focuses on the explainability of the obtained representations and results. The data source for our research is plWordNet, which is the wordnet of the Polish language and includes a rich set of derivation examples.
%U https://aclanthology.org/2023.gwc-1.39
%P 325-330
Markdown (Informal)
[Wordnet-oriented recognition of derivational relations](https://aclanthology.org/2023.gwc-1.39) (Walentynowicz & Piasecki, GWC 2023)
ACL
- Wiktor Walentynowicz and Maciej Piasecki. 2023. Wordnet-oriented recognition of derivational relations. In Proceedings of the 12th Global Wordnet Conference, pages 325–330, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.