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223 citations found. Retrieving citations...
Context Doc 18 (0): Reinhard Kneser and Hermann Ney. Improved backing-off for m-gram language modeling. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, volume I, pages 181--184, Detroit, Michigan, May 1995.
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Context Doc 15 (0): Hermann Ney, Ute Essen, and Reinhard Kneser. On structuring probabilistic dependences in stochastic language modeling. Computer Speech and Language, 8:1--38, 1994.
Context Doc 13 (0): Martin Oerder and Hermann Ney. 1993. Word graphs: An efficient interface between continuousspeech recognition and language understanding. In ICASSP Volume 2, pages 119--122.
Context Doc 12 (2): Franz Josef Och, Christoph Tillmann, Hermann Ney, "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. University of Maryland, College Park, MD, June 1999.
Context Doc 8 (2): Franz Josef Och and Hermann Ney. 2000. Improved statistical alignment models. In Proceedings of ACL2000, pages 440--447.
Context Doc 7 (0): Reinhard Kneser and Hermann Ney. Improved ClusteringTechniques for Class-based Statistical Language Modelling. In Proceedings EUROSPEECH, pp. 973--976, 1993.
Context Doc 7 (0): Reinhard Kneser and Hermann Ney. Improved Smoothing for M-gram Language Modeling. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Detroit, MI, May 1995.
Context Doc 6 (0): Ney, Hermann, Essen, Ute and Kneser, Reinhard, (1994) "On Structuring Probabilistic Dependencies in Stochastic Language Modeling," in Computer, Speech and Language, Vol. 8, pp. 1-38.
Context Doc 6 (5): Stephan Vogel, Hermann Ney, and Christoph Till-mann, "HMM-based Word Alignment in Statistical Translation," Proceedings of COLING '96: The 16th International Conference on Computational Linguistics, pp. 836-841. Copenhagen, August 1996.
Context Doc 6 (0): Franz Josef Och and Hermann Ney, "A Comparison of Alignment Models for Statistical Machine Translation," Proceedings of the 18th International Conference on Computational Linguistics, Saarbrucken, Germany, July 2000.
Context Doc 6 (0): Xavier Aubert and Hermann Ney. Large vocabulary continuous speech recognition using word graphs. Proc. ICASSP, pages 49--52, 1995.
Context Doc 5 (1): Reinhard Kneser and Hermann Ney. Forming Word Classes by Statistical Clustering for Statistical Language Modeling. In Proceedings of the 1st QUALICO Conference, Trier, Germany, September 1991.
Context Doc 5 (0): Franz Josef Och and Hermann Ney. 2003. A systematic comparison on various statistical alignment models.
Context Doc 5 (0): NEY,HERMANN. 1984. The use of a one-stage dynamic programming algorithm for connected word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 32.263--271.
Context Doc 4 (0): Hermann Ney, "Dynamic Programming Parsing for Context-Free Grammars in Continuous Speech Recognition," IEEE Transactions on Signal Processing, 39(2), February 1991.
Context Doc 4 (0): S. Vogel, Hermann Ney, and C. Tillmann. 1996. Hmm based word alignment in statistical machine translation. In Proc. The 16th Int. Conf. on Computational Lingustics, (Coling'96), pages 836--841, Copenhagen, Denmark.
Context Doc 4 (0): Franz Josef Och and Hermann Ney. 2002. Discriminative training and maximum entropy models for statistical machine translation. In Proceedings of ACL.
Context Doc 3 (0): Ismael Garca-Varea, Francisco Casacuberta, and Hermann Ney. An iterative, DP-based search algorithm for statistical machine translation. In ICSLP-98
Context Doc 3 (0): Hermann Ney, Ute Essen, and Reinhard Kneser. On the estimation of `small' probabilities by leaving-one-out. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12):1202--1212, December 1995.
Context Doc 3 (0): Stephan Kanthak and Hermann Ney. Context-dependent acoustic modeling using graphemes for large vocabulary speech recognition. In IEEE Signal Processing Society, ICASSP in Orlando FL, pages 845--848, 2002.
Context Doc 2 (0): Harald Aust and Hermann Ney. 1998. Evaluating dialog systems used in the real world. In Proc. IEEE ICASSP, volume 2, pages 1053--1056.
Context Doc 2 (0): Sven Martin, Joerg Liebermann, and Hermann Ney. Algorithms for bigram and trigram clustering. In Eurospeech, 1995.
Context Doc 2 (1): Christoph Tillmann, Stephan Vogel, Hermann Ney, and Alex Zubiaga. 1997. A DP-based search using monotone alignments in statistical translation. In Proc. 35th Annual Conf. of the Association for Computational Linguistics, pages 289--296, Madrid, Spain, July.
Context Doc 2 (0): Ney, Hermann., "On structuring probabilistic dependency in stochastic language modeling", Computer Speech & Language 8: 1-38, 1994.
Context Doc 2 (0): Hermann Ney, `Acoustic modeling of phoneme units for continuous speech recognition', in Fifth Europ. Signal Processing Conference (Barcelona (Spanien), pp. 65--72, (September 1990).
Context Doc 2 (0): Franz Josef Och and Hermann Ney. Statistical Machine Translation. In Proceedings of the European Association for Machine Translation Workshop, EAMT'00, pages 39-46, Ljubljana, Slove- nia, May 2001. http://nl.ijs.si/eamt00/.
Context Doc 2 (2): Sonja Nieen, Franz Josef Och, Gregor Leusch, Hermann Ney. 2000. An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research. In Proceedings of LREC, Athens, Greece, May .
Context Doc 1 (0): Exponential language models, logisitc regression, and semantic coherence. In Proceedings of NIST/DARPA Speech Transciption Workshop. Reinhard Kneser and Hermann Ney. 1995. Improved backing-o for m-gram language modeling.
Context Doc 1 (0): Franz-Josef Och and Hermann Ney. 2001. Statistical multi-source translation. In Proceedings of MT Summit VIII, Santiago de Compostela, Spain, September.
Context Doc 1 (0): In AMTA, pages 115--124. Philipp Koehn. Unpublished. Europarl: A multilingual corpus for evaluation of machine translation. Draft. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models.
Context Doc 1 (0): In COLING Proceedings, pages 1086--1090, Jul. Franz Josef Och and Hermann Ney. 2000b. Improved statistical alignment models. In ACL Proceedings, pages 440--447, Oct.
Context Doc 1 (0): Richard Zens, Hermann Ney, Taro Watanabe, and Eiichiro Sumita. Reordering constraints for phrase-based statistical machine translation. In Proceedings of COLING, Geneva, August 2004.
Context Doc 1 (0): Richard Zens and Hermann Ney. A comparative study on reordering constraints in statistical machine translation. pages 192--202, Hong Kong, August 2003.
Context Doc 1 (0): Gregor Leusch, Nicola Ueffing, and Hermann Ney. A novel string-to-string distance measure with applications to machine translation evaluation. In Machine Translation Summit, New Orleans, 2003.
Context Doc 1 (0): Nicola Ueffing, Franz Josef Och, and Hermann Ney. 2002. Generation of word graphs in statistical machine translation. In Proc. of the Conference on Empirical Methods in Natural Language Processing, Philadephia, PA, July 6-7.
Context Doc 1 (1): Franz Josef Och and Hermann Ney. 2004. The alignment template approach to statistical machine translation. Computational Linguistics, 30(4).
Context Doc 1 (0): Hermann Ney, Ute Essen, and Reinhard Kneser. On the estimation of 'small' probabilities by leaving-one-out. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12):1202-1212, December 1995. 34
Context Doc 1 (0): Hermann Ney and Stefan Ortmanns, "Dynamic programming search for continuous speech recognition, " IEEE Signal Processing Magazine, vol. 16, no. 5, pp. 64--83, 1999.
Context Doc 1 (0): Stephan Vogel and Hermann Ney. Translation with cascaded nite state transducers. In ACL-00 [1], pages 23-36.
Context Doc 1 (0): Martin Oerder and Hermann Ney. Word graphs: An e#cient interface between continuous-speech recognition and language understanding. In ICASSP Volume 2, pages 119--122, 1993.
Context Doc 1 (0): Ralf Schlueter, Boris Mueller, Frank Wessel, and Hermann Ney. Interdependence of language models and discriminative training. IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings (ASRU), pages 119-122, 1999.
Context Doc 1 (0): Hermann Ney, Dieter Mergel, Andreas Noll, and Annedore Paeseler. Data driven search organization for continuous speech recognition. IEEE Trans. on Signal Processing, 40(2):272--281, February 1992.
Context Doc 1 (0): Hermann Ney, "Modeling and search in continuous speech recognition", in Proceedings Eurospeech 93, pp. 491-494. 1993.
Context Doc 1 (0): Lutz Welling, Nils Haberland, and Hermann Ney, "Acoustic front-end optimization for large vocabulary speech recognition, " in Proc. Eurospeech'97, Rhodes, Greece, September
Context Doc 1 (1): Sonja Nieen, Stephan Vogel, Hermann Ney, and Christoph Tillmann. 1998. A DP-based search algorithm for statistical machine translation. In COLING-ACL '98: 36th Annual Meeting of the Association for Computational Linguistics and 17th Int. Conf. on Computational Linguistics, pages 960--967, Montreal, Canada, August.
Context Doc 1 (0): Hermann Ney. The Use of a One#Stage Dynamic Programming Algorithm for Connected Word Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32 #2#: 263#271, April 1984.
Context Doc 1 (0): Sven Martin, Jorg Liermann, and Hermann Ney. 1998. Algorithms for bigram and trigram word clustering. Speech Communication, 24(1):19-- 37.
Context Doc 1 (0): Summer Research Workshop Technical Reports 30, Johns Hopkins University, Baltimore, MD. Kneser, Reinhard and Hermann Ney. 1995. Improved backing-o for mgram language modeling. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, volume 1, pages 181{ 184.
Context Doc 1 (0): Hermann Ney. Architecture and Search Strategies for Large-Vocabulary Continuous-Speech Recognition., pages 59-- 84. NAT)-ASI Bubi'on, 1993.
Context Doc 1 (0): Hermann Ney. Stochastic grammars and pattern recognition. In P. Laface and R. DeMori, editors, Speech Recognition and Understanding, pages 319--344. Springer, Berlin, 1992.
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The numbers before each article are the number of citations (excluding self-citations), and the predicted number of self-citations. 223 citations were found, of which 36 were predicted to be self-citations. Self-citations are not included in the graph.
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