Extended Constituent-to-Dependency Conversion for English

By Richard Johansson, Pierre Nugues. Nordic Conference of Computational Linguistics, 2007

331 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/570cd4cedd3ec5b51035fadebebaa88ac91f8b45

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Citations in the corpus

An Unsupervised Model for Instance Level Subcategorization Acquisition (2014, methodology): “parser is transformed to the CoNLL dependency format using the official CoNLL 2007 conversion script (Johansson and Nugues, 2007); and then (c) the SCF of each verb instance is inferred using the rule-based system used by (Reichart and Korhonen, 2013).”
What’s in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation (2015, methodology): “, 1994), converted using the LTH converter (Johansson and Nugues, 2007), was used as the corresponding manually annotated resource.”
A Latent Variable Model for Generative Dependency Parsing (2007, methodology): “We also evaluated the ISBN parser on all ten languages considered in the CoNLL2007 shared task (Hajič et al., 2004; Aduriz et al., 2003; Martí et al., 2007; Chen et al., 2003; Böhmová et al., 2003; Marcus et al., 1993; Johansson and Nugues, 2007; Prokopidis et al., 2005; Csendes et al., 2005; Montem”
The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies (2008, methodology): “Our conversion procedure (Johansson and Nugues, 2007) differs from this basic approach by exploiting the rich structure of the constituent format used in Penn Treebank 3:”
The Effect of Syntactic Representation on Semantic Role Labeling (2008, methodology): “All parsers were trained on the Penn Tree-bank, either directly for the constituent parsers or through the LTH constituent-to-dependency converter (Johansson and Nugues, 2007).”
Tailoring Continuous Word Representations for Dependency Parsing (2014, methodology): “10 For both treebanks, we convert from constituent to dependency format using pennconverter (Johansson and Nugues, 2007), and generate POS tags using the MXPOST tagger (Ratnaparkhi, 1996).”
Discriminative Syntax-Based Word Ordering for Text Generation (2015, unknown): “Thedata are based on the CoNLL 2008 shared task data (Surdeanu et al. 2008), which consist of selected sections of the Penn WSJ Treebank, converted to syntactic dependencies via the LTH tool (Johansson and Nugues 2007).”
Parser Evaluation over Local and Non-Local Deep Dependencies in a Large Corpus (2011, methodology): “The model we evaluate is the second-order projective model trained on the same WSJ corpus, where the original PTB phrase structure annotations were ﬁrst converted into dependencies, as established in the CoNLL shared task 2009 (Johansson and Nugues, 2007).”
Frustratingly Hard Domain Adaptation for Dependency Parsing (2007, methodology): “We were given around 15K sentences of labeled text from the Wall Street Journal (WSJ) (Marcus et al., 1993; Johansson and Nugues, 2007) as well as 200K unlabeled sentences.”
The First Surface Realisation Shared Task: Overview and Evaluation Results (2011, methodology): “For the preparation of the CoNLL-08 Shared task data, selected sections of the Penn WSJ Treebank were converted to syntactic dependencies via the LTH Constituent-to-Dependency Conversion Tool for Penn-style Treebanks (Pennconverter) (Johansson and Nugues, 2007).”
FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers (2017, methodology): “The current generator has been mainly developed on the dependency Penn Tree-bank (Johansson and Nugues, 2007) automatically converted to predicate-argument structures, and adapted to the AMR inputs using SemEval’s training and evaluation sets.”
Generative Incremental Dependency Parsing with Neural Networks (2015, background): “Constituency trees are converted to projective CoNLL syntactic dependencies (Johansson and Nugues, 2007) with the LTH converter2.”
The CoNLL 2007 Shared Task on Dependency Parsing (2007, methodology): “To convert these sets to dependency structures we used the same procedure as before (Johansson and Nugues, 2007a).”