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1.
Technical-term translation represents one of the most difficult tasks for human translators since (1) most translators are not familiar with terms and domain-specific terminology and (2) such terms are not adequately covered by printed dictionaries. This paper describes an algorithm for translating technical words and terms from noisy parallel corpora across language groups. Given any word which is part of a technical term in the source language, the algorithm produces a ranked candidate match for it in the target language. Potential translations for the term are compiled from the matched words and are also ranked. We show how this ranked list helps translators in technical-term translation. Most algorithms for lexical and term translation focus on Indo-European language pairs, and most use a sentence-aligned clean parallel corpus without insertion, deletion or OCR noise. Our algorithm is language- and character-set-independent, and is robust to noise in the corpus. We show how our algorithm requires minimum preprocessing and is able to obtain technical-word translations without sentence-boundary identification or sentence alignment, from the English–Japanese awk manual corpus with noise arising from text insertions or deletions and on the English–Chinese HKUST bilingual corpus. We obtain a precision of 55.35% from the awk corpus for word translation including rare words, counting only the best candidate and direct translations. Translation precision of the best-candidate translation is 89.93% from the HKUST corpus. Potential term translations produced by the program help bilingual speakers to get a 47% improvement in translating technical terms.  相似文献   
2.
This paper describes a framework for modeling the machine transliteration problem. The parameters of the proposed model are automatically acquired through statistical learning from a bilingual proper name list. Unlike previous approaches, the model does not involve the use of either a pronunciation dictionary for converting source words into phonetic symbols or manually assigned phonetic similarity scores between source and target words. We also report how the model is applied to extract proper names and corresponding transliterations from parallel corpora. Experimental results show that the average rates of word and character precision are 93.8% and 97.8%, respectively.  相似文献   
3.
基于实例的机器翻译系统需要双语句对的支持。为大量获取双语句对,则需要以篇章对齐的双语文本为输入,实现句子的自动对齐。通过分析汉英双语法律文本的特征,提出了法律文本对齐假设。首先识别出法规源文和译文中的结构标识和句子,然后在句子一级对齐法律文本。该方法在150篇汉英法律文本语料上,取得了80.98%的对齐准确率。  相似文献   
4.
该文提出了一种从搜索引擎返回的结果网页中获取双语网页的新方法,该方法分为两个任务。第一个任务是自动地检测并收集搜索引擎返回的结果网页中的数据记录。该步骤通过聚类的方法识别出有用的记录摘要并且为下一个任务即高质量双语混合网页的验证及其获取提供有效特征。该文中把双语混合网页的验证看作是有效的分类问题,该方法不依赖于特定领域和搜索引擎。基于从搜索引擎收集并经过人工标注的2 516条检索结果记录,该文提出的方法取得了81.3%的精确率和94.93%的召回率。  相似文献   
5.
该文提出了一种从英汉平行语料库中自动抽取术语词典的算法。采用的是已对齐好的双语语料,中文经过了分词处理。利用英文和中文词性标注工具对英文语料和中文语料分辨进行词性标注。统计双语语料库中的名词和名词短语生成候选术集。然后对每个英文候选术语计算与其相关的中文翻译间的翻译概率。再通过设定阈值过滤掉一些与该英文候选词无关的中文翻译,最后通过贪心算法选取概率最大的词作为该英文候选词的中文翻译。  相似文献   
6.
该文提出了一种从英汉平行语料库中自动抽取术语词典的算法。采用的是已对齐好的双语语料,中文经过了分词处理。利用英文和中文词性标注工具对英文语料和中文语料分辨进行词性标注。统计双语语料库中的名词和名词短语生成候选术集。然后对每个英文候选术语计算与其相关的中文翻译间的翻译概率。再通过设定阈值过滤掉一些与该英文候选词无关的中文翻译,最后通过贪心算法选取概率最大的词作为该英文候选词的中文翻译。  相似文献   
7.
基于Web的双语平行句对自动获取   总被引:3,自引:1,他引:2  
双语平行句对是机器翻译的重要资源,但是由于获取途径的限制,句子级平行语料库不仅数量有限而且经常集中在特定领域,很难适应真实应用的需求。该文介绍了一个基于Web的双语平行句对自动获取系统。该系统融合了现有系统的优点,对其中的关键技术进行了改进。文中提出了一种自动发现双语网站中URL命名规律的方法,改进了双语平行句对抽取技术。实验结果表明文中所提出的方法大大提高了候选双语网站发现的召回率,所获取双语平行句对的召回率为93%,准确率为96%,证明了该文方法的有效性。此外,该文还对存在于双语对照网页内部的双语平行句对的抽取方法进行了研究,取得了初步成果。  相似文献   
8.
Sentence alignment using P-NNT and GMM   总被引:2,自引:0,他引:2  
Parallel corpora have become an essential resource for work in multilingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross-language information retrieval and machine translation applications. In this paper, we present two new approaches to align English–Arabic sentences in bilingual parallel corpora based on probabilistic neural network (P-NNT) and Gaussian mixture model (GMM) classifiers. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the probabilistic neural network and Gaussian mixture model. Another set of data was used for testing. Using the probabilistic neural network and Gaussian mixture model approaches, we could achieve error reduction of 27% and 50%, respectively, over the length based approach when applied on a set of parallel English–Arabic documents. In addition, the results of (P-NNT) and (GMM) outperform the results of the combined model which exploits length, punctuation and cognates in a dynamic framework. The GMM approach outperforms Melamed and Moore’s approaches too. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese–Chinese texts, than the ones used in the current research.  相似文献   
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