Query Translation in Chinese-English Cross-Language Information Retrieval

Query Translation in Chinese-English Cross-Language Information Retrieval Zhang Yibo, Sun Le, Du Lin, Sun Yufang Chinese Information Processing Center, Institute of Software, Chinese Academy of Sciences, P.O.Box 8718, Beijing, 100080, P.R. China e-mail: zyb, lesun, !du, yfsun@sonata.iscas.ac.cn Abstract This paper proposed a new query translation method based on the mutual information matrices of terms in the Chinese and English corpora. Instead of looking up a • bilingual phrase dictionary, the compositional phrase (the translation of phrase can be derived from the translation of its components) in the query can be indirectly translated via a general-purpose Chinese-English dictionary look-up procedure. A novel selection method for translations of query terms is also presented in detail. Our query translation method ultimately constructs an English query in which each query term has a weight. The evaluation results show that the retrieval performance achieved by our query translation method is about 73% of monolingual information retrieval and is about 28% higher than that of simple word- by-word translation way. Introduction With the rapid growth of electronic documents and the great development of network in China, there are more and more people touching the Intemet, on which, however, English is the most popular language being used. It is difficult for most people in China to use English fluently, so they would like to use Chinese to express their queries to retrieval the relevant English documents. This situation motivates research in Cross Language Information Retrieval (CLIR). There are two approaches to CLIR, one is query translation; the other is translating original language documents to destination This research was supported by the National Science Fund of China for Distinguished Young Scholars under contact 69983009. language equivalents. Obviously, the latter is a very expensive task since there are so many documents in a collection and there is not yet a reliable machine translation system that can be used to process automatically. Most researchers are inclined to choose the query translation approach [Oard. (1996)]. Methods for query translation have focused on three areas: the employment of machine translation techniques, dictionary based translation [Hull & Grefenstette (1996); Ballesteros & Croft (1996)], parallel or comparable corpora for generating a translation model [Davis & Dunning (1995); Sheridan & Ballerini (1996); Nie, Jian-Yun et a1.(1999)]. Machine translation (MT) method has many obstacles to prevent its employment into CLIR such as deep syntactic and semantic analysis, user queries consisting of only one or two words, and an arduous task to build a MT system. Dictionary based query translation is the most popular method because of its easiness to perform. The main reasons leading to the great drops in CLIP,. effectiveness by this method are ambiguities caused by more than one translation of a query term and failures to translate phrases during query translation. Previous studies [Hull & Grefenstette (1996); Ballesteros & Croft (1996)] have shown that automatic word-by- word (WBW) query translation via machine readable dictionary (MKD) results in a 40-60% loss in effectiveness below that of monolingual retrieval. With regard to the use of …

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