Comparing and Combining Finite-State and Context-Free Parsers

By Kristy Hollingshead, Seeger Fisher, Brian Roark. 2005

27 citations recorded by the source corpus.

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 comparisons greatly under-estimated the performance of context-free parsers for these tasks. We also demonstrate that context-free parsers can train effectively on relatively little training data, and are more robust to domain shift for shallow parsing tasks than has been previously reported. Finally, we establish that combining 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 corresponding cost in accuracy is higher than previously thought.

Subfields: Artificial Intelligence, Information Extraction, Computational Linguistics & Semantics

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Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, methodology): “As in Hollingshead et al. (2005), consider the sentence and chunk labels “(NP They) (VP are starting to buy) (NP growth stocks)”.”