Noun phrase chunking in Hebrew: influence of lexical and morphological features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
Integrated morphological and syntactic disambiguation for Modern Hebrew
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Relational-realizational parsing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Modeling morphosyntactic agreement in constituency-based parsing of modern Hebrew
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
On the role of morphosyntactic features in Hindi dependency parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Easy first dependency parsing of modern Hebrew
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Getting more from morphology in multilingual dependency parsing
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We describe a newly available Hebrew Dependency Treebank, which is extracted from the Hebrew (constituency) Tree-bank. We establish some baseline unlabeled dependency parsing performance on Hebrew, based on two state-of-the-art parsers, MST-parser and MaltParser. The evaluation is performed both in an artificial setting, in which the data is assumed to be properly morphologically segmented and POS-tagged, and in a real-world setting, in which the parsing is performed on automatically segmented and POS-tagged text. We present an evaluation measure that takes into account the possibility of incompatible token segmentation between the gold standard and the parsed data. Results indicate that (a) MST-parser performs better on Hebrew data than Malt-Parser, and (b) both parsers do not make good use of morphological information when parsing Hebrew.