2009 CoNLL Shared Task Part 1
|Item Name:||2009 CoNLL Shared Task Part 1|
|Author(s):||Jan Hajič, Maria Antonia Martí, Lluis Marquez, Joakim Nivre, Jan Štěpánek, Sebastian Padó, Pavel Straňák|
|LDC Catalog No.:||LDC2012T03|
|Release Date:||April 20, 2012|
|Application(s):||semantic role labelling, syntactic parsing|
|Language(s):||Spanish, German, Czech, Catalan|
|Language ID(s):||spa, deu, ces, cat|
CoNLL 2009 Shared Task Data
|Online Documentation:||LDC2012T03 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Hajič, Jan , et al. 2009 CoNLL Shared Task Part 1 LDC2012T03. Web Download. Philadelphia: Linguistic Data Consortium, 2012.|
|Related Works: Hide||View|
2009 CoNLL Shared Task Part 1, LDC Catalog Number LDC2012T03 and ISBN 1-58563-610-X, contains the Catalan, Czech, German and Spanish trial corpora, training corpora, development and test data for the 2009 CoNLL (Conference on Computational Natural Language Learning) Shared Task Evaluation. The 2009 Shared Task developed syntactic dependency annotations, including the semantic dependencies model roles of both verbal and nominal predicates.
The Conference on Computational Natural Language Learning (CoNLL) is accompanied every year by a shared task intended to promote natural language processing applications and evaluate them in a standard setting. The 2004 and 2005 CoNLL shared tasks were dedicated to semantic role labeling (SRL) in a monolingual setting (English). In 2006 and 2007, the shared tasks were devoted to the parsing of syntactic dependencies and used corpora from up to thirteen languages. In 2008, the shared task focused on English and employed a unified dependency-based formalism and merged the task of syntactic dependency parsing and the task of identifying semantic arguments and labeling them with semantic roles that data has been released by LDC as 2008 CoNLL Shared Task Data. The 2009 task extended the 2008 task to several languages (English plus Catalan, Chinese, Czech, German, Japanese and Spanish). Among the new features were comparison of time and space complexity based on participants input, and learning curve comparison for languages with large datasets.
The 2009 shared task was divided into two subtasks:
- parsing syntactic dependencies
- identification of arguments and assignment of semantic roles for each predicate
2009 CoNLL Shared Task Part 2 (LDC2012T04) contains the English and Chinese task data and is also available through LDC.
LDC has also released the following CoNLL Shared Task data sets:
- 2006 CoNLL Shared Task - Ten Languages (LDC2015T11)
- 2006 CoNLL Shared Task - Arabic & Czech (LDC2015T12)
- 2008 CoNLL Shared Task Data (LDC2009T12)
- 2015-2016 CoNLL Shared Task (LDC2017T13)
The materials in this release consist of excerpts from the following corpora:
- Ancora (Spanish + Catalan): 500,000 words each of annotated news text developed by the University of Barcelona, Polytechnic University of Catalonia, the University of Alacante and the University of the Basque Country
- Prague Dependency Treebank 2.0 (Czech): approximately 2 million words of annotated news, journal and magazine text developed by Charles University also available through LDC, LDC2006T01
- TIGER Treebank + SALSA Corpus (German): approximately 900,000 words of annotated news text and FrameNet annotation developed by the University of Potsdam, Saarland University and the University of Stuttgart
In addition, an archive of all of the uploaded data from the participants is included in the eval-data folder. Users should note that not all data indicated in the individual READMEs is included in this release and neither are some of the corresponding DTDs for of the XML. Additionally, all data is presented in its uncompressed form for ease of use. Within the user eval-data folder, the two folders marked bad contain references to data from languages included in Part 2 of this release as well as to Japanese data. Japanese data is not included in this release.
For samples of documents from each language use the links below:
None at this time.