臺灣博碩士論文加值系統

本論文提出一個利用平行語料庫抽取雙語搭配詞的方法。我們的方法整合統計以及語言資訊，能有效地截取出雙語搭配詞。整合詞性、詞塊、子句三種語言分析，可以精準地抽取出英文之搭配詞結構，例如：動詞與名詞之搭配詞、形容詞與名詞之搭配詞等。本論文中所提出的方法是利用大型的單語語料庫之統計藉以輔助小型雙語語料庫中count的不足，進而得到不同的搭配詞。之後，再從雙語語料庫中採用詞彙對應以及雙語詞典資訊，來抽取搭配詞翻譯。經由實驗評估後，驗證本文所提出的方法能有效抽取正確之搭配詞和相對應之翻譯，並可以有效地使用在電腦輔助語言學習以及電腦輔助翻譯等應用上。

In this paper, we propose a new method for extracting bilingual collocation from a parallel corpus. The method integrates statistical and linguistic information for effective extraction of bilingual collocations. The method involves first obtaining an extended list of distinct English collocations from a very large monolingual corpus, identifying the collocation instances in a parallel corpus, and extracting translation equivalent of the collocations based on word alignment information. At run time, collocations in the parallel corpus are identified and aligned to the translation equivalent. Experimental results show our method is efficient for learning translation memory of collocations. We applied the method to develop a collocational concordancer, TANGO, which showed great potential for applications in Computer Assisted Language Leaning and Computer Assisted Translation.

摘要 i
Abstract ii
Table of Content iii
Chapter 1 Introduction 1
Chapter 2 Related Work 6
Chapter 3 Extraction of Collocational Translation Memory 9
3.1 Problem Statement 9
3.2 Taggers for Parts of Speech, Chunks, and Clauses 11
3.3 Extraction of Collocation Types in M 14
3.4 Extraction of Collocation Instances in PC 18
3.5 Extracting Collocation Translation Equivalents in Bilingual Corpus 19
Chapter 4 Implementation and Evaluation 20
4.1 Performance of the Chunker and Clauser 20
4.2 Evaluation of the Collocation Extraction 21
4.3 Evaluation of the Collocation Translation 24
Chapter 5 Discussion 28
Chapter 6 Conclusion 31
References 32
Appendix 35
Top 100 vn collocation extracted from BNC 35
Top 100 vnp collocation extracted from BNC 38
Top 100 vpn collocation extracted from BNC 42

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