1、1,Using the Web for Automated Translation Extraction in Cross-Language Information Retrieval,Advisor : Dr. Hsu Presenter : Zih-Hui Lin Author :Ying Zhang and Phil Vines,2,Motivation Objective Previous work Methodology Experiments and results Conclusions,Outline,3,Motivation,One of the major remainin

2、g reasons that CLIR does not perform as well as monolingual retrieval is the presence of out of vocabulary (OOV) terms. it will not be recognized, and segmented into either smaller sequences of characters or individual characters 北野武(north limit military) Previous work has either relied on manual in

3、tervention or has only been partially successful in solving this problem.,4,Objective,We propose a segmentation free method which can be applied to both Chinese-English and English-Chinese CLIR, correctly extracting translations of OOV terms from the Web automatically, and thus is a significant impr

4、ovement on earlier work,5,English translation extraction in Chinese-English CLIR,Chinese OOV term detection 北野武(north limit military) Pvalue given by the HMM will be very low if Pvalue Pmin contains OOV terms web text extraction we extract strings that contain the Chinese query terms and some Englis

5、h text from the Web. collection of co-occurrence statistics, translation selection.,北野武（Kitano Takeshi）c4 c5 c6 e 1 導(dǎo)演北野武 （ Kitano Takeshi）c2 c3 c4 c5 c6 e1,6,Chinese translation extraction in English-Chinese CLIR,Extraction of web text use Google to fetch the top100 Chinese documents with the Engli

6、sh OOV term eoov as the query. Collection of co-occurrence statistics accumulate the frequency foov. considering all substrings in Sleft and Sright, and collecting the frequency fn and the length |sn| of each Chinese substring. Translation selection exclude any substring that already in the translat

7、ion dictionary doesnt occur in the document collection,7,Experiments and results,Chinese-English CLIR,English-Chinese CLIR,8,Introduction,When translating from Chinese to English, a standard first step is to segment the text into words based on an existing segmentation dictionary. However where an O

8、OV term occurs, it will not be recognized, and segmented into either smaller sequences of characters or individual characters. We propose a segmentation free method based on frequency and length analysis and corpus-based disambiguation,9,Previous work,Dictionary-based translation schemes need to add

9、ress three major issues phrase identification and translation ex. non proliferation treaty and cross straits. translation ambiguity using techniques such as term co-occurrence , mutual information or language modeling. out of vocabulary (OOV) terms. ex. Dioxin,10,Previous work- Existing approaches t

10、o the OOV problem,Depending on the language, it may be possible to deduce appropriate transliterated translations automatically. that they successfully applied in English-Arabic CLIR. However the issue is more difficult in Chinese as many characters have the same sound, and many English syllables do

11、 not have equivalent sounds in Chinese, meaning that selecting the correct characters to represent a transliterated word can be problematic. cross straits(兩岸)、北野武(north limit military),11,Previous work- Segmentation free translation extraction,It is common to find a small amount of English text in C

12、hinese web documents, but extremely rare to find Chinese text in English web documents. We therefore rely on Chinese web documents to extract translations in both directions. The problem is that the Chinese OOV term we are looking for is currently unknown, and thus we have no information about how i

13、t should be segmented. In previous work, this problem was overcome by manual intervention to provide appropriate segmentation.,12,Experiments and results,Chinese-English CLIR retrieving English documents using Chinese queries.,13,Experiments and results (cont.),English-Chinese CLIR retrieving Chinese documents using English queries. The aim of our work is to find appropriate Chinese translations of English OOV terms,14,Conclusions,We have also described improved ways to extract the translation of OOV terms from the Web in a way that does not rely on prior segmentation. Although