本文選題：信息檢索 + 機(jī)器翻譯　； 參考：《哈爾濱工程大學(xué)學(xué)報(bào)》2017年03期

【摘要】：本文將基于單語語料的檢索技術(shù)運(yùn)用到機(jī)器翻譯中,構(gòu)建了一個(gè)漢英譯文推薦系統(tǒng),解決傳統(tǒng)方法雙語料庫構(gòu)建代價(jià)高昂的問題,同時(shí)提高最終譯文的流暢性。譯文推薦系統(tǒng)包括查詢翻譯和信息檢索兩部分:查詢翻譯根據(jù)給定的一組中文,生成N-best英文結(jié)果;信息檢索評(píng)價(jià)目標(biāo)語言與候選譯文的相似程度。系統(tǒng)綜合兩部分得分返回推薦譯文�？紤]到N-best結(jié)果與候選譯文的詞序一致性,采用Levenshtein距離使得排序結(jié)果更加合理。在英漢數(shù)據(jù)集上的實(shí)驗(yàn)表明:在不同n階語言模型下,譯文推薦系統(tǒng)都有很好的表現(xiàn),加入Levenshtein距離取得了最高70.83%的f測度值。
[Abstract]:In this paper, we apply monolingual corpus retrieval technology to machine translation and construct a Chinese-English translation recommendation system to solve the expensive problem of traditional dual-corpus construction and to improve the fluency of the final translation. The translation recommendation system includes two parts: query translation and information retrieval: query translation generates N-best English results according to a given set of Chinese; information retrieval evaluates the similarity between target language and candidate translation. The system synthesizes the score of the two parts and returns the recommended translation. Considering the consistency between N-best result and candidate translation, Levenshtein distance is used to make the result more reasonable. The experiments on English and Chinese datasets show that the translation recommendation systems have a good performance under different n-order language models, and the maximum f measure value is 70.83% when Levenshtein distance is added.
【作者單位】： 北京工業(yè)大學(xué)計(jì)算機(jī)學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61133003)
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 陳士杰,張sソ，

本文編號(hào)：2004887