A systematic comparison of various statistical alignment models
Computational Linguistics
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Bootstrapping statistical parsers from small datasets
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Inducing multilingual text analysis tools via robust projection across aligned corpora
HLT '01 Proceedings of the first international conference on Human language technology research
Bootstrapping parsers via syntactic projection across parallel texts
Natural Language Engineering
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Mildly non-projective dependency structures
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
LTAG dependency parsing with bidirectional incremental construction
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Non-projective dependency parsing in expected linear time
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Dependency grammar induction via bitext projection constraints
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
An efficient algorithm for easy-first non-directional dependency parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Dependency parsing and projection based on word-pair classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Recent work has shown how a parallel corpus can be leveraged to build syntactic parser for a target language by projecting automatic source parse onto the target sentence using word alignments. The projected target dependency parses are not always fully connected to be useful for training traditional dependency parsers. In this paper, we present a greedy non-directional parsing algorithm which doesn't need a fully connected parse and can learn from partial parses by utilizing available structural and syntactic information in them. Our parser achieved statistically significant improvements over a baseline system that trains on only fully connected parses for Bulgarian, Spanish and Hindi. It also gave a significant improvement over previously reported results for Bulgarian and set a benchmark for Hindi.