Demographic-aware word associations

Aparna Garimella, Carmen Banea, Rada Mihalcea


Abstract
Variations of word associations across different groups of people can provide insights into people’s psychologies and their world views. To capture these variations, we introduce the task of demographic-aware word associations. We build a new gold standard dataset consisting of word association responses for approximately 300 stimulus words, collected from more than 800 respondents of different gender (male/female) and from different locations (India/United States), and show that there are significant variations in the word associations made by these groups. We also introduce a new demographic-aware word association model based on a neural net skip-gram architecture, and show how computational methods for measuring word associations that specifically account for writer demographics can outperform generic methods that are agnostic to such information.
Anthology ID:
D17-1242
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2285–2295
Language:
URL:
https://www.aclweb.org/anthology/D17-1242
DOI:
10.18653/v1/D17-1242
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1242.pdf