BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Neural Network Language Models for Translation with Limited Data
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Case markers and morphology: addressing the crux of the fluency problem in English-Hindi SMT
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 2 - Volume 2
International Journal of Knowledge Engineering and Soft Data Paradigms
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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This paper describes the usage of case markers in Hindi-Urdu grammar and implementation of translation rules for case marking in our English to Hindi/Urdu machine translation system. Case marking is one of the sensitive and important parts in machine translation for grammatical relation and semantic point of view. In this study, we are discussing the case marker, postpositions and their effect on nouns, pronouns and verbs. We have created and implemented translations rules and achieved n-gram BLEU score: 0.6985, METEOR score: 0.8599 and F-measure score: 0.8714 which is an improvement to our system's previous MT evaluation scores.