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Translating noun compounds using semantic relations

机译：使用语义关系翻译名词化合物

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摘要

Despite having a research history of more than 20 years, English to Hindi machine translation often suffers badly from incorrect translations of noun compounds. The problems envisaged can be of various types, such as, the absence of proper postpositions, inappropriate word order, incorrect semantics. Different existing English to Hindi machine translation systems show their vulnerability, irrespective of the underlying technique. A potential solution to this problem lies in understanding the semantics of the noun compounds. The present paper proposes a scheme based on semantic relations to address this issue. The scheme works in three steps: identification of the noun compounds in a given text, determination of the semantic relationship(s) between them, and finally, selecting the right translation pattern. The scheme provides translation patterns for different semantic relations for 2-word noun compounds first. These patterns are used recursively to find the semantic relations and the translation patterns for 3-word and 4-word noun compounds. Frequency and probability based adjacency and dependency models are used for bracketing (grouping) the constituent words of 3-word and 4-word noun compounds into 2-word noun compounds. The semantic relations and the translation patterns generated for 2-word, 3-word and 4-word noun compounds are evaluated. The proposed scheme is compared with some well-known English to Hindi translators, viz. AnglaMT, Anuvadaksh, Bing, Google, and also with the Moses baseline system. The results obtained, show significant improvement over the Moses baseline system. Also, it performs better than the other online MT systems in terms of recall and precision.
机译：尽管有20多年的研究历史，但英语到北印度语的机器翻译常常因名词化合物的不正确翻译而遭受严重的折磨。设想的问题可能有多种类型，例如缺少适当的后置位，不适当的词序，不正确的语义。不论底层技术如何，现有的不同的英语到北印度语机器翻译系统都显示出它们的漏洞。该问题的潜在解决方案在于理解名词化合物的语义。本文提出了一种基于语义关系的方案来解决这个问题。该方案的工作分为三个步骤：识别给定文本中的名词化合物，确定它们之间的语义关系，最后选择正确的翻译模式。该方案首先为2词名词化合物提供了针对不同语义关系的翻译模式。递归使用这些模式来查找3词和4词名词复合词的语义关系和翻译模式。基于频率和概率的邻接关系和依存关系模型用于将3词和4词名词复合词的构成词括入括号（分组）为2词名词复合词。评估了2词，3词和4词名词复合词的语义关系和翻译模式。将提议的方案与一些著名的英语到印地语翻译进行比较，即。 AnglaMT，Anuvadaksh，Bing，Google，以及Moses基准系统。获得的结果表明，与Moses基准系统相比，有了显着的改进。而且，在召回率和准确性方面，它比其他在线MT系统要好。

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