Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Experiments with a Hindi-to-English transfer-based MT system under a miserly data scenario
ACM Transactions on Asian Language Information Processing (TALIP)
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Exploiting a probabilistic hierarchical model for generation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Dependency treelet translation: syntactically informed phrasal SMT
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Robust PCFG-based generation using automatically acquired LFG approximations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Dependency-based n-gram models for general purpose sentence realisation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
Probabilistic models for disambiguation of an HPSG-based chart generator
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Glue rules for robust chart realization
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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In this paper, we present five models for sentence realisation from a bag-of-words containing minimal syntactic information. It has a large variety of applications ranging from Machine Translation to Dialogue systems. Our models employ simple and efficient techniques based on n-gram Language modeling. We evaluated the models by comparing the synthesized sentences with reference sentences using the standard BLEU metric (Papineni et al., 2001). We obtained higher results (BLEU score of 0.8156) when compared to the state-of-art results. In future, we plan to incorporate our sentence realiser in Machine Translation and observe its effect on the translation accuracies.