The Secret Lives of Names? Name Embeddings from Social Media
Table 5. Average Prediction Error (in years) of seven sets of models using different features. The demographic features are: birth year (B), state (S), gender (G), ethnicity (E), nationality (N). Extra features include: no embedding (NoEbd), shuffled embedding (ShEbd), Email embedding (EmEbd), Twitter embedding (TwEbd). Each number (or prediction error) is the average of 20 runs. Birth year is the most important feature due to survivorship bias. Using name embeddings improves performance significantly.
['[ITALIC] B', '[ITALIC] S', '[ITALIC] G', '[ITALIC] E', '[ITALIC] N', '[ITALIC] NoEbd', '[ITALIC] ShEbd', '[ITALIC] EmEbd', '[ITALIC] TwEbd']
We use 90% records as training data and use the rest as testing data. Due to survivorship bias, the most powerful single feature is the birth year, which yields an absolute error of 8.052 years. The strength of birth year feature separates models into two groups, with/without birth year