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The CoNLL 2007 Shared Task on Dependency Parsing
The tasks of the different tracks are defined and how the data sets were created from existing treebanks for ten languages are described, to characterize the different approaches of the participating systems and report the test results and provide a first analysis of these results.
MaltParser: A Language-Independent System for Data-Driven Dependency Parsing
Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.
Stylebook for the Tubingen Treebank of Written German (TuBa-D/Z)
This stylebook is an updated version of Telljohann et al. (2006), the most recent and most complete version of the TüBa-D/Z treebank, and focuses on the syntactic annotation of written language data taken from the German newspaper ’die tageszeitung’ (taz).
The Tüba-D/Z Treebank: Annotating German with a Context-Free Backbone
The comparison between the annotation schemes of the two treebanks focuses on the different treatments of free word order and discontinuous constituents in German as well as on differences in phrase-internal annotation.
SAMAR: Subjectivity and sentiment analysis for Arabic social media
Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
- Djamé Seddah, Reut Tsarfaty, Eric Villemonte de la Clergerie
- Computer ScienceSPMRL@EMNLP
- 18 October 2013
This paper presents and analyzes parsing results obtained by the task participants, and provides an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios.
This book surveys the three major classes of parsing models that are in current use: transition- based, graph-based, and grammar-based models, and gives a thorough introduction to the methods that are most widely used today.
Statistical Parsing of Morphologically Rich Languages (SPMRL) What, How and Whither
This paper synthesizes the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages and suggests itself as a source of directions for future investigations.
Introducing the SPMRL 2014 Shared Task on Parsing Morphologically-rich Languages
This paper provides a short overview of the 2014 SPMRL shared task goals, data sets, and evaluation setup and describes the description of participating systems and the analysis of their results as part of (Seddah et al., 2014).
Is it Really that Difficult to Parse German?
Parser performance for the models trained on TuBa-D/Z are comparable to parsing results for English with the Stanford parser, when trained on the Penn treebank, suggesting that German is not harder to parse than its West-Germanic neighbor language English.