Paper: Hidden-Variable Models For Discriminative Reranking

ACL ID H05-1064
Title Hidden-Variable Models For Discriminative Reranking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005

We describe a new method for the repre- sentation of NLP structures within rerank- ing approaches. We make use of a condi- tional log–linear model, with hidden vari- ables representing the assignment of lexi- cal items to word clusters or word senses. The model learns to automatically make these assignments based on a discrimina- tive training criterion. Training and de- coding with the model requires summing over an exponential number of hidden– variable assignments: the required sum- mations can be computed efficiently and exactly using dynamic programming. As a case study, we apply the model to parse reranking. The model gives an F– measure improvement of ≈ 1.25% be- yond the base parser, and an ≈ 0.25% improvement beyond the Collins (2000) reranker. Although our experiments a...