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
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2285–2295
- Language:
- URL:
- https://aclanthology.org/D17-1242
- DOI:
- 10.18653/v1/D17-1242
- Cite (ACL):
- Aparna Garimella, Carmen Banea, and Rada Mihalcea. 2017. Demographic-aware word associations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2285–2295, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Demographic-aware word associations (Garimella et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1242.pdf