Skip to Main content Skip to Navigation
New interface
Conference papers

Boosting for Efficient Model Selection for Syntactic Parsing

Abstract : We present an efficient model selection method using boosting for transition-based constituency parsing. It is designed for exploring a high-dimensional search space, defined by a large set of feature templates, as for example is typically the case when parsing morphologically rich languages. Our method removes the need to manually define heuristic constraints, which are often imposed in current state-of-the-art selection methods. Our experiments for French show that the method is more efficient and is also capable of producing compact, state-of-the-art models.
Document type :
Conference papers
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : Rachel Bawden Connect in order to contact the contributor
Submitted on : Thursday, December 22, 2016 - 3:55:04 PM
Last modification on : Thursday, November 3, 2022 - 3:30:26 AM
Long-term archiving on: : Tuesday, March 21, 2017 - 5:48:43 AM


Files produced by the author(s)


  • HAL Id : hal-01391743, version 2


Rachel Bawden, Benoît Crabbé. Boosting for Efficient Model Selection for Syntactic Parsing. COLING 2016 - 26th International Conference on Computational Linguistics, Dec 2016, Osaka, Japan. pp.1-11. ⟨hal-01391743v2⟩



Record views


Files downloads