The CoNLL 2007 Shared Task on Dependency Parsing

By Joakim Nivre, Johan Hall, Sandra Kübler, Ryan T. McDonald, Jens Nilsson, Sebastian Riedel, Deniz Yuret. Conference on Empirical Methods in Natural Language Processing, 2007

776 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/10a9abb4c78f0be5cc85847f248d3e8277b3c810

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Citations in the corpus

Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (2018, methodology): “We ran a similar experiment using biomedical and chemistry text, taken from the unannotated data provided by (Nivre et al., 2007).”
An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing (2010, background): “An important part of this research effort are the CoNLL 2006 and 2007 shared tasks (Buch-holz and Marsi, 2006; Nivre et al., 2007), which allowed for a comparison of many algorithms and approaches for this task on many languages.”
Approximation-Aware Dependency Parsing by Belief Propagation (2015, methodology): “Data We consider 19 languages from the CoNLL-2006 (Buchholz and Marsi, 2006) and CoNLL-2007 (Nivre et al., 2007) Shared Tasks.”
Distributed Word Representation Learning for Cross-Lingual Dependency Parsing (2014, methodology): “We used the dataset from the CoNLL shared task (Buchholz and Marsi, 2006; Nivre et al., 2007) for cross-lingual dependency parsing.”
Polyglot: Distributed Word Representations for Multilingual NLP (2013, background): “Despite recent momentum towards developing multilingual tools (Nivre et al., 2007; Hajič et al., 2009; Pradhan et al., 2012), most of NLP research still focuses on rich resource languages.”
Stack-Pointer Networks for Dependency Parsing (2018, methodology): “There are two dominant approaches to dependency parsing (Buchholz and Marsi, 2006; Nivre et al., 2007): local and greedy transitionbased algorithms (Yamada and Matsumoto, 2003; Nivre and Scholz, 2004; Zhang and Nivre, 2011; Chen and Manning, 2014), and the globally optimized graph-based algorithms (”
Deep multi-task learning with low level tasks supervised at lower layers (2016, background): “However, in NLP it is natural to think of some levels of analysis as feeding into others, typically with low-level tasks feeding into high-level ones; e.g., POS tags as features for syntactic chunking (Sang and Buchholz, 2000) or parsing (Nivre et al., 2007).”
Universal Dependency Annotation for Multilingual Parsing (2013, background): “In particular, the CoNLL shared tasks on dependency parsing have provided over twenty data sets in a standardized format (Buch-holz and Marsi, 2006; Nivre et al., 2007).”
Linguistically Naïve != Language Independent: Why NLP Needs Linguistic Typology (2009, background): “Nivre and McDonald (2008) present an approach to dependency parsing which integrates graph-based and transition-based meth-ods, and evaluate the result against the 13 datasets ing our ideas against particular languages. provided in the CoNLL-X shared task (Nivre et al., 2007).”
Introducing the SPMRL 2014 Shared Task on Parsing Morphologically-rich Languages (2014, result): “Compared to previous multilingual shared tasks (Buchholz and Marsi, 2006; Nivre et al., 2007), the SPMRL shared task targeted parsing in realistic evaluation scenarios, in which the analysis of morphologically ambiguous input tokens is not known in advance.”
The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages (2009, background): “for the 2007 shared task on multilingual dependency parsing (Nivre et al., 2007)6, substantial differences existed in the distribution of the predicates and arguments, the input features, the out-of-vocabulary rates, and other statistical characteristics of the data.”
A Latent Variable Model of Synchronous Syntactic-Semantic Parsing for Multiple Languages (2009, background): “Recent research in syntax-based statistical machine translation and the recent availability of syntactically annotated corpora for multiple languages (Nivre et al., 2007) has provided a new opportunity for evaluating the cross-linguistic validity of statistical models of syntactic structure.”
Syntactic Processing Using the Generalized Perceptron and Beam Search (2011, methodology): “In the CoNLL shared tasks on dependency parsing (Buchholz and Marsi 2006; Nivre et al. 2007) most data sets contain only 1–2% non-projective arcs, and projective dependency parsing models can give reasonable accuracy in these tasks (Carreras, Surdeanu, and Marquez 2006).”
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017, methodology): “The Google dependency representation pre-dates Universal Dependencies, deriving from the scheme used by McDonald et al. (2013), i.e., Stanford Dependencies 2.0 with the option to make copula verbs heads (de Marneffe and Manning, 2008, section 4.7) and Google Universal POS tags (Petrov et al., 2011).”
Frustratingly Hard Domain Adaptation for Dependency Parsing (2007, background): “This paper outlines our participation in the 2007 CoNLL Shared Task on Domain Adaptation (Nivre et al., 2007).”