Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations
This work addresses the problem of improving natural language processing models by personalizing them based on demographic factors, which is incremental as it builds on existing association models.
The paper analyzed human word association pairs to study the correlation between response types and respondent characteristics like gender and occupation, and proposed a personalized distributed word association model that shows incorporating demographic factors improves NLP models.
We present a quantitative analysis of human word association pairs and study the types of relations presented in the associations. We put our main focus on the correlation between response types and respondent characteristics such as occupation and gender by contrasting syntagmatic and paradigmatic associations. Finally, we propose a personalised distributed word association model and show the importance of incorporating demographic factors into the models commonly used in natural language processing.