Paper: An Unsupervised Model for Instance Level Subcategorization Acquisition

ACL ID D14-1034
Title An Unsupervised Model for Instance Level Subcategorization Acquisition
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Most existing systems for subcategoriza- tion frame (SCF) acquisition rely on su- pervised parsing and infer SCF distribu- tions at type, rather than instance level. These systems suffer from poor portability across domains and their benefit for NLP tasks that involve sentence-level process- ing is limited. We propose a new unsuper- vised, Markov Random Field-based model for SCF acquisition which is designed to address these problems. The system relies on supervised POS tagging rather than parsing, and is capable of learning SCFs at instance level. We perform eval- uation against gold standard data which shows that our system outperforms several supervised and type-level SCF baselines. We also conduct task-based evaluation in the context of verb similarity prediction, demonstrating that a ...