Y
Yi Zhang
Researcher at Saarland University
Publications - 56
Citations - 1335
Yi Zhang is an academic researcher from Saarland University. The author has contributed to research in topics: Parsing & Head-driven phrase structure grammar. The author has an hindex of 15, co-authored 56 publications receiving 1280 citations. Previous affiliations of Yi Zhang include German Research Centre for Artificial Intelligence & Shanghai Jiao Tong University.
Papers
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Proceedings ArticleDOI
The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages
Jan Hajiċ,Massimiliano Ciaramita,Richard Johansson,Daisuke Kawahara,Maria Antònia Martí,Lluís Màrquez,Adam Meyers,Joakim Nivre,Sebastian Padó,Jan Štėpánek,Pavel Straňák,Mihai Surdeanu,Nianwen Xue,Yi Zhang +13 more
TL;DR: This shared task combines the shared tasks of the previous five years under a unique dependency-based formalism similar to the 2008 task and describes how the data sets were created and show their quantitative properties.
Broad-Coverage Semantic Dependency Parsing
Stephan Oepen,Marco Kuhlmann,Yusuke Miyao,Daniel Zeman,Dan Flickinger,Jan Haji,Angelina Ivanova,Yi Zhang +7 more
TL;DR: This task description position the problem in comparison to other sub-tasks in computational language analysis, introduce the semantic dependency target representations used, reflect on high-level commonalities and differences between these representations, and summarize the task setup, participating systems, and main results.
Proceedings Article
Validation and Evaluation of Automatically Acquired Multiword Expressions for Grammar Engineering
TL;DR: The overall conclusion is that at least two measures seem to differentiate MWEs from non-MWEs, and it is argued that such a process improves qualitatively, if a more compositional approach to grammar/lexicon automated extension is adopted.
Proceedings ArticleDOI
Automated Multiword Expression Prediction for Grammar Engineering
TL;DR: This paper proposes to semi-automatically detect MWE candidates in texts using some error mining techniques and validating them using a combination of the World Wide Web as a corpus and some statistical measures to provide a significant increase in the coverage of these expressions.
Efficiency in Unification-Based N -Best Parsing.
TL;DR: A recently proposed algorithm for n-best unpacking of parse forests is extended to deal efficiently with Maximum Entropy parse selection models containing important classes of non-local features and forests produced by unification grammars containing significant proportions of globally inconsistent analyses.