Interactive Semantic Featuring for Text Classification

In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teacher, and present results showing that models built using these human-comprehensible features are competitive with models trained with Bag of Words features.

Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (2004)

This is the Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, which was held in Banff, Canada, July 7 - 11 2004.