Paper: Comparing And Combining Finite-State And Context-Free Parsers

ACL ID H05-1099
Title Comparing And Combining Finite-State And Context-Free Parsers
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

In this paper, we look at comparing high- accuracy context-free parsers with high- accuracy finite-state (shallow) parsers on several shallow parsing tasks. We show that previously reported compar- isons greatly under-estimated the perfor- mance of context-free parsers for these tasks. We also demonstrate that context- free parsers can train effectively on rel- atively little training data, and are more robust to domain shift for shallow pars- ing tasks than has been previously re- ported. Finally, we establish that combin- ing the output of context-free and finite- state parsers gives much higher results than the previous-best published results, on several common tasks. While the efficiency benefit of finite-state models is inarguable, the results presented here show that the correspondin...