@inproceedings{mccrae-2018-mapping,
title = "Mapping {W}ord{N}et Instances to {W}ikipedia",
author = "McCrae, John P.",
editor = "Bond, Francis and
Vossen, Piek and
Fellbaum, Christiane",
booktitle = "Proceedings of the 9th Global Wordnet Conference",
month = jan,
year = "2018",
address = "Nanyang Technological University (NTU), Singapore",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2018.gwc-1.8/",
pages = "61--68",
abstract = "Lexical resource differ from encyclopaedic resources and represent two distinct types of resource covering general language and named entities respectively. However, many lexical resources, including Princeton WordNet, contain many proper nouns, referring to named entities in the world yet it is not possible or desirable for a lexical resource to cover all named entities that may reasonably occur in a text. In this paper, we propose that instead of including synsets for instance concepts PWN should instead provide links to Wikipedia articles describing the concept. In order to enable this we have created a gold-quality mapping between all of the 7,742 instances in PWN and Wikipedia (where such a mapping is possible). As such, this resource aims to provide a gold standard for link discovery, while also allowing PWN to distinguish itself from other resources such as DBpedia or BabelNet. Moreover, this linking connects PWN to the Linguistic Linked Open Data cloud, thus creating a richer, more usable resource for natural language processing."
}
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%0 Conference Proceedings
%T Mapping WordNet Instances to Wikipedia
%A McCrae, John P.
%Y Bond, Francis
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 9th Global Wordnet Conference
%D 2018
%8 January
%I Global Wordnet Association
%C Nanyang Technological University (NTU), Singapore
%F mccrae-2018-mapping
%X Lexical resource differ from encyclopaedic resources and represent two distinct types of resource covering general language and named entities respectively. However, many lexical resources, including Princeton WordNet, contain many proper nouns, referring to named entities in the world yet it is not possible or desirable for a lexical resource to cover all named entities that may reasonably occur in a text. In this paper, we propose that instead of including synsets for instance concepts PWN should instead provide links to Wikipedia articles describing the concept. In order to enable this we have created a gold-quality mapping between all of the 7,742 instances in PWN and Wikipedia (where such a mapping is possible). As such, this resource aims to provide a gold standard for link discovery, while also allowing PWN to distinguish itself from other resources such as DBpedia or BabelNet. Moreover, this linking connects PWN to the Linguistic Linked Open Data cloud, thus creating a richer, more usable resource for natural language processing.
%U https://aclanthology.org/2018.gwc-1.8/
%P 61-68
Markdown (Informal)
[Mapping WordNet Instances to Wikipedia](https://aclanthology.org/2018.gwc-1.8/) (McCrae, GWC 2018)
ACL
- John P. McCrae. 2018. Mapping WordNet Instances to Wikipedia. In Proceedings of the 9th Global Wordnet Conference, pages 61–68, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.