Data processing: speech signal processing – linguistics – language – Linguistics – Translation machine
Reexamination Certificate
2003-03-27
2008-11-18
Dorvil, Richemond (Department: 2626)
Data processing: speech signal processing, linguistics, language
Linguistics
Translation machine
C704S004000, C704S242000, C704S277000
Reexamination Certificate
active
07454326
ABSTRACT:
A machine translation (MT) system may utilize a phrase-based joint probability model. The model may be used to generate source and target language sentences simultaneously. In an embodiment, the model may learn phrase-to-phrase alignments from word-to-word alignments generated by a word-to-word statistical MT system. The system may utilize the joint probability model for both source-to-target and target-to-source translation applications.
REFERENCES:
patent: 5867811 (1999-02-01), O'Donoghue
patent: 6236958 (2001-05-01), Lange et al.
patent: 6502064 (2002-12-01), Miyahira et al.
Kenji Yamada and kevin Knight “A Syntax-based Statistical translation Model”, 39th Annual Meeting for the Assocation for Computational Linguistics, 2001, pp. 1-8.
Brown, et al., “The Mathematics of Statistical Machine Translation: Parameter Estimation”,Computational Linguistics, 19(2):263-311 (1993); XP008022787.
Melamed, “Word-to-Word Models of Translational Equivalence”,Empirical Methods for Exploiting Parallel Texts, The MIT Press, Mar. 2001; XP002280151.
Marcu, “Towards a Unified Approach to Memory- and Statistical-Based Machine Translation”,Proceedings of ACL-2001, Toulouse, France, Jul. 2001 <http://www.isi.edu/˜marcu/papers/transmem-acl101.pdf>; XP002280148.
Marcu, et al., “A Phrase-Based, Joint Probability Model for Statistical Machine Translation”,Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP-2002), Philadelphia, PA, Jul. 2002 <http://www.isi.edu/˜marcu/papers/jointmt2002.pdf>; XP002280146.
Och, et al., “Improved Alignment Models for Statistical Machine Translation”,Proceedings of the Joint Conference of Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 20-28, Univ. of Maryland, College Park, MD, Jun. 1999 <http://acl/ldc/upenn.edu/W/W99/W99-0604.pdf>; XP002280147.
Tillmann, et al., “A Phrase-based Unigram Model for Statistical Machine Translation”,HLT-NAACL 2003, Edmonton, Canada, May 2003 <http://acl/ldc/upenn.edu/N/N03/N03-2036.pdf>; XP002280149.
Vogel, et al., “The CMU Statistical Machine Translation System”,Machine Translation Summit IX, New Orleans, LA, Sep. 2003, <http://www.amtaweb.org/summit/MTSummit/FinalPapers/105-vogel-final.pdf>; XP002280150.
Knight Kevin
Koehn Philipp
Marcu Daniel
Wong William
Carr & Ferrell LLP
Dorvil Richemond
Siedler Dorothy S
University of Southern California
LandOfFree
Phrase to phrase joint probability model for statistical... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Phrase to phrase joint probability model for statistical..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Phrase to phrase joint probability model for statistical... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4046487