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Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations, pages 39–43, Dublin, Ireland, August 23-29 2014. Lightweight Client-Side Chinese/Japanese Morphological Analyzer Based on Online Learning Masato Hagiwara Satoshi Sekine Rakuten Institute of Technology, New York 215 Park Avenue South, New York, NY {masato.hagiwara, satoshi.b.sekine}@ma

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 79–83, Gothenburg, Sweden, April 26-30 2014. c 2014 Association for Computational Linguistics Deterministic Word Segmentation Using Maximum Matching with Fully Lexicalized Rules Manabu Sassano Yahoo Japan Corporation Midtown Tower, 9-7-1 Akasaka, Minato-ku, Tokyo 107-6211, Japan msass

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 207–217, Sofia, Bulgaria, August 4-9 2013. c 2013 Association for Computational Linguistics Unsupervised Transcription of Historical Documents Taylor Berg-Kirkpatrick Greg Durrett Dan Klein Computer Science Division University of California at Berkeley {tberg,gdurrett,klein}@cs.berkeley.edu Abstract We p

2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 688–698, Montréal, Canada, June 3-8, 2012. c 2012 Association for Computational Linguistics Unified Expectation Maximization Rajhans Samdani University of Illinois rsamdan2@illinois.edu Ming-Wei Chang Microsoft Research minchang@microsoft.com Dan Roth University of Il

Scaling to Very Very Large Corpora for Natural Language Disambiguation Michele Banko and Eric Brill Microsoft Research 1 Microsoft Way Redmond, WA 98052 USA {mbanko,brill}@microsoft.com Abstract The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compa

Proceedings of the Workshop on Advances in Text Input Methods (WTIM 2011), pages 19–25, Chiang Mai, Thailand, November 13, 2011. Efficient dictionary and language model compression for input method editors Taku Kudo, Toshiyuki Hanaoka, Jun Mukai, Yusuke Tabata, and Hiroyuki Komatsu Google Japan Inc. {taku,toshiyuki,mukai,tabata,komatsu}@google.com Abstract Reducing size of dictionary and language

UNSUPERVISED WORD SENSE DISAMBIGUATION RIVALING SUPERVISED METHODS David Yarowsky Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA yarowsky~unagi, ci s.upenn, edu Abstract This paper presents an unsupervised learn- ing algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniqu

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 1–10, Singapore, 6-7 August 2009. c 2009 ACL and AFNLP Unsupervised Semantic Parsing Hoifung Poon Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {hoifung,pedrod}@cs.washington.edu Abstract We present the first unsupervised approach to

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 496–504, Singapore, 6-7 August 2009. c 2009 ACL and AFNLP Multi-Class Confidence Weighted Algorithms Koby Crammer∗ ∗ Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {crammer,kulesza}@cis.upenn.edu Mark Dredze† Alex Kulesza∗ † Human Language Technology Cente

Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 858–867, Prague, June 2007. c 2007 Association for Computational Linguistics Large Language Models in Machine Translation Thorsten Brants Ashok C. Popat Peng Xu Franz J. Och Jeffrey Dean Google, Inc. 1600 Amphitheatre Parkway Mountain View, CA 94303, USA {br