Enhancing cross-language information retrieval by an automatic acquisition of bilingual terminology from comparable corpora

Author(s):  
Fatiha Sadat ◽  
Masatoshi Yoshikawa ◽  
Shunsuke Uemura
Terminology ◽  
2001 ◽  
Vol 7 (1) ◽  
pp. 63-83 ◽  
Author(s):  
Hiroshi Nakagawa

Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.


2014 ◽  
Vol 687-691 ◽  
pp. 1683-1686
Author(s):  
Shuang Wang

This thesis proposes several methods for bilingual corpus form different websites, such as Automatic acquisition of bilingual corpus base on "iciba" web, CNKI and Patent network. It introduced methods, procedures of the acquisition of a variety of corpus. We proposed different methods to obtain the bilingual corpus for different characteristics of different sites, and achieved fast and accurate automatic access of a large-scale bilingual corpus. When we obtain the bilingual corpus based on "iciba" web, the main method is Nutch crawler, which is relatively good, and has an accurate retrieve and a good correlation. In addition, we give up the idea of bilingual corpus obtained from the entire Internet, but we use an entirely new access, that is to access to the basic information of scholarly thesis’s in the CNKI to obtain the large-scale high-quality English-Chinese bilingual corpus. We obtain GB level of large-scale bilingual aligned corpus in the end, which is very accurate by the manual evaluation. And the corpus makes preparation for the further cross-language information retrieval research.


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