@inproceedings{reyes-magana-etal-2020-automatic,
title = "Automatic Word Association Norms ({AWAN})",
author = "Reyes-Maga{\~n}a, Jorge and
Sierra Mart{\'\i}nez, Gerardo and
Bel-Enguix, Gemma and
Gomez-Adorno, Helena",
editor = "Zock, Michael and
Chersoni, Emmanuele and
Lenci, Alessandro and
Santus, Enrico",
booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.cogalex-1.17",
pages = "142--153",
abstract = "Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.",
}
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<abstract>Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.</abstract>
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%0 Conference Proceedings
%T Automatic Word Association Norms (AWAN)
%A Reyes-Magaña, Jorge
%A Sierra Martínez, Gerardo
%A Bel-Enguix, Gemma
%A Gomez-Adorno, Helena
%Y Zock, Michael
%Y Chersoni, Emmanuele
%Y Lenci, Alessandro
%Y Santus, Enrico
%S Proceedings of the Workshop on the Cognitive Aspects of the Lexicon
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F reyes-magana-etal-2020-automatic
%X Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.
%U https://aclanthology.org/2020.cogalex-1.17
%P 142-153
Markdown (Informal)
[Automatic Word Association Norms (AWAN)](https://aclanthology.org/2020.cogalex-1.17) (Reyes-Magaña et al., CogALex 2020)
ACL
- Jorge Reyes-Magaña, Gerardo Sierra Martínez, Gemma Bel-Enguix, and Helena Gomez-Adorno. 2020. Automatic Word Association Norms (AWAN). In Proceedings of the Workshop on the Cognitive Aspects of the Lexicon, pages 142–153, Online. Association for Computational Linguistics.