Luận văn:Integrated linguistic to Statistical Machine Translation = Tích hợp thông tin ngôn ngữ vào dịch máy tính thống kê. Luận văn ThS. Công nghệ thông tin: 60 48 01
Nhà xuất bản:ĐHCN
Ngày:2012
Chủ đề:Khoa học máy tính
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Miêu tả:35 p. + CD-ROM + tóm tắt
Luận văn ThS. Khoa học máy tính -- Trường Đại học Công nghệ. Đại học Quốc gia Hà Nội, 2012
In the field of Natural Language Processing, automatic machine translation is an attractive application for a supporting user to translate some sentences in a language to others. Today, Phrase-based Statistical Machine Translation is the-state-of-the-art with benefit in the word choosing, distortion based on the distance between words. However, we still have some problem with global dis-tortion model of different languages (long distance between words). In some previous studies, the linguistic information such as a syntax tree, morphology information or hierarchical of phrase is used. Similarly, we also use the syntax tree to Giúp the distortion model. However, instead of using full parse tree, we use a shallow syntax tree (the height of tree is limited). By using some trans-formation rules, we can arrange the order of some nodes in the shallow syntax tree. Hence, we reorder the words in the sentence. A special point in our study is applying the transformation rule on the sentence in the source language to get new sentence with new order of words, which is similar with the target language, as preprocessing step before training translation model or decoding with beam search and log linear model. The experiment results from an English-Vietnamese pair showed that our approach achieves significant improvements over MOSES which is the state-of-the-art phrase based system
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00051000109_Noi_dung.pdf

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