WebTable 5: Performance of the first-stage parser on various combinations of distributions WSJ and WSJ+NANC (self-trained) models on sections 1, 22, and 24. Distributions are L (left expansion), R (right expansion), H (head word), M (head phrasal category), and T (head POS tag). ∗ and ⊛ indicate the model is not significantly different from baseline and self … WebEffective Self-Training for Parsing David McClosky, Eugene Charniak, and Mark Johnson Brown Laboratory for Linguistic Information Processing (BLLIP) Brown University …
Reranking and Self-Training for Parser Adaptation
WebJan 1, 2009 · Effective self-training for parsing. In. HLT-NAACL. David McClosky, Eugene Charniak, and Mark John-son. 2008. When is self-training effective for pars-ing? In COLING. Slav Petrov and Dan Klein. 2007. Webmerit, it remains unclear why self-training helps in some cases but not others. Our goal is to better un-derstand when and why self-training is beneficial. In Section 2, we discuss the previous applica-tions of self-training to parsing. Section 3 de-scribes our experimental setup. We present and test four hypotheses of why self-training helps in st they\u0027d
Effective Self-Training for Parsing - Semantic Scholar
Webself-training helps self-training helps self-training doesn't help Phase Transition accuracy (f-score) sections 1, 22, 24 Parser 85.8% 10% WSJ Parser 89.9% 100% WSJ Reranking Parser 87.0% 10% WSJ Reranking Parser 91.5% 100% WSJ There is no phase transition for self-training. See also: Reichart and Rappoport (2007) WebDOI: 10.3115/1220835.1220855 Corpus ID: 628455; Effective Self-Training for Parsing @inproceedings{McClosky2006EffectiveSF, title={Effective Self-Training for Parsing}, author={David McClosky and Eugene Charniak and Mark Johnson}, booktitle={North American Chapter of the Association for Computational Linguistics}, year={2006} } WebNov 1, 2024 · Earlier attempts failed to prove effectiveness of self-training for dependency parsing [Rush et al. 2012]. ... We present a simple yet effective self-training approach, named as STAD, for low ... st thibault 10