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基于音节首字母匹配的音译单元对齐方法 被引量：1

Transliteration Unit Alignment Method Based on the First Syllable Letter Mapping
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摘要 音译涉及的两种语言采用不同的字母表和发音系统时(英语和汉语,英语和日语,英语和阿拉伯语等),机器音译就更复杂且更具有挑战性。音译单元对齐结果的好坏严重影响机器音译的准确率,为此研究了英汉机器音译中音译单元的对齐,提出了基于音节首字母匹配的音译单元对齐方法,该方法在音译单元的对齐中有较好的表现。 It is very complicated and challenging to translate names and technical terms across languages that employ very different alphabets and sound systems, such as English/Chinese, English/Japanese and Arabic/English and so on. The quality of transliteration units alignment seriously affects the precision of Machine Transliteration. In our research, we concentrate on the transliteration unit alignment and propose a novel method for transliteration unit alignment which is called transliteration unit alignment based on the first letter. This method has a good performance in transliteration alignment.
作者 赵明明 梁颖红 周美玲 姚建民
机构地区 江苏省现代企业信息化应用支撑软件工程技术研究开发中心 苏州大学计算机科学与技术学院
出处 《江南大学学报（自然科学版）》 CAS 2009年第6期639-642,共4页 Joural of Jiangnan University (Natural Science Edition)　
基金 国家自然科学基金项目(60970057) 江苏省现代企业信息化应用支撑软件工程技术研究与开发中心开放项目(SX200907)
关键词 音译单元 机器音译 VITERBI算法 N—gram模型 transliteration unit, machine transliteration, viterbi algorithm, n-gram mode
分类号 TP391.1 [自动化与计算机技术—计算机应用技术]
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参考文献9

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同被引文献20

  • 1黄昌宁,赵海.中文分词十年回顾[J].中文信息学报,2007,21(3):8-19. 被引量：250
  • 2Hnang F, Vogel S, Waibel A. Automatic extraction of named entity translingual equivalence based on multi-feature cost mini- mization[ A]. Proceedings of the ACL 2003 Workshop on MUI- tilingual and Mixed-Language Named Entity Recognition-Vol- ume 15 [ C ]. Stroudsburg, PA, USA: Association for Computa- tional Linguistics, 2003.9- 16.
  • 3Feng D, Lti Y, Zhou M. A new approach for English-Chinese named entity alignment[ A]. EMNLP. 2004E C1. Barcelona: As- sociation for Computational Linguistics,2004. 372- 379.
  • 4Lee C J,Chang J S,Jang J S R. Alignment of bilingual named entities in parallel corpora using statistical models and multiple knowledge sourees[ J] .ACM Transactions on Asian Language Information Processing(TALIP), 2006,5 (2) : 121 - 145.
  • 5Moore R C. Learning translations of named-entity phrases from parallel corpora [ A ]. Proceedings of the tenth Conference on European Chapter of the Association for Computational Lin- guistics- Volume 1 [ C ]. Budapest: Association for Computational Linguistics, 2003.259 - 266.
  • 6Ji H, Grishman R. Collaborative entity extraction and translation [J]. Recent Advances in Natural Language Processing V:Se- lected Papers from RANLP 2007,2009,309:73 - 84.
  • 7Che W, Wang M, Manning C D, et al. Named entity recogni- tion with bilingual constraints[ A] Proceedings of HLT-NAA- CL [ C ]. Atlanta: Association for Computational Linguistics, 2013.52 - 62.
  • 8Wang M, Che W, Manning C D. Joint word alignment and bilingual named entity recognition using dual decomposition [ A]. Proceedings of the 51 st Annual Meeting of the Associa- tion for Computational Linguistics [ C ]. Sofia: Association for Computational Linguistics,2013.1073- 1082.
  • 9Chen Y, Zong C, Su K Y. A joint model to identify and align bilingual named entities[ J]. Computational Linguistics, 2013,39 (2) : 229 - 266.
  • 10Chang P C, Galley M, Manning C D. Optimizing Chinese word segmentation for machine lranslation performance[ A ]. Proceedings of the Third Workshop on Statistical Machine Translation. Association for Computational Linguistics E C. 2008. 224 - 232.

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江南大学学报（自然科学版）
2009年 第6期

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