| Introduction | In this paper we present experiments with an automatic system for semantic role labeling (SRL) that is designed to model aspects of human language acquisition . |
| Introduction | Proposed solutions to this problem in the NLP and human language acquisition literatures focus on distributional learning as a key data source (e.g., (Mintz, 2003; Johnson, 2007)). |
| Model | The architecture of our system is similar to a previous approach to modeling early language acquisition (Connor et al., 2009), which is itself based on the standard architecture of a full SRL system (e.g. |