Browsing by Author "Wang, Jianqiang"
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Item Comparing User-assisted and Automatic Query Translation(2003-02-27) He, Daqing; Wang, Jianqiang; Oard, Douglas W.; Nossal, MichaelFor the 2002 Cross-Language Evaluation Forum Interactive Track, the University of Maryland team focused on query formulation and reformulation. Twelve people performed a total of forty eight searches in the German document collection using English queries. Half of the searches were with user-assisted query translation, and half with fully automatic query translation. For the user-assisted query translation condition, participants were provided two types of cues about the meaning of each translation: a list of other terms with the same translation (potential synonyms), and a sentence in which the word was used in a translation-appropriate context. Four searchers performed the official iCLEF task, the other eight searched a smaller collection. Searchers performing the official task were able to make more accurate relevance judgments with user-assisted query translation for three of the four topics. We observed that the number of query iterations seems to vary systematically with topic, system, and collection, and we are analyzing query content and ranked retrieval measures to obtain further insight into these variations in search behavior. UMIACS-TR-2003-23 LAMP-TR-098 HCIL-TR-2003-07Item Matching Meaning for Cross-Language Information Retrieval(2005-12-06) Wang, Jianqiang; Oard, Douglas W; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cross-language information retrieval concerns the problem of finding information in one language in response to search requests expressed in another language. The explosive growth of the World Wide Web, with access to information in many languages, has provided a substantial impetus for research on this important problem. In recent years, significant advances in cross-language retrieval effectiveness have resulted from the application of statistical techniques to estimate accurate translation probabilities for individual terms from automated analysis of human-prepared translations. With few exceptions, however, those results have been obtained by applying evidence about the meaning of terms to translation in one direction at a time (e.g., by translating the queries into the document language). This dissertation introduces a more general framework for the use of translation probability in cross-language information retrieval based on the notion that information retrieval is dependent fundamentally upon matching what the searcher means with what the document author meant. The perspective yields a simple computational formulation that provides a natural way of combining what have been known traditionally as query and document translation. When combined with the use of synonym sets as a computational model of meaning, cross-language search results are obtained using English queries that approximate a strong monolingual baseline for both French and Chinese documents. Two well-known techniques (structured queries and probabilistic structured queries) are also shown to be a special case of this model under restrictive assumptions.Item NTCIR CLIR Experiments at the University of Maryland(2000-06-21) Oard, Douglas W.; Wang, JianqiangThis paper presents results for the Japanese/English cross-language informaiton retrieval task on teh NACSIS Test Collection. Two automatic dictionary-based query translation techniques were tried with four variants of the queries. The results indicate that longer queries outperform the required description only queries and that use of the first translation in the edict dictionary is comparable with the use of every translation. Japanese term segmentation posed no unusual problems, which contrasts sharply with results previously obtained for corss-language retrieval between Chinese and English. (Also cross-referenced as UMIACS-TR-2000-47, LAMP-TR-054)Item TREC-8 Experiments at Maryland: CLIR, QA and Routing(2000-06-21) Oard, Douglas W.; Wang, Jianqiang; Lin, Dekang; Soboroff, IanThe University of Maryland team participated in four aspects of TREC-8: the ad hoc retrieval task, the main task in the cross-language retrieval (CLIR) track, the question answering track, and the routing task in the filtering track. The CLIR method was based on Pirkola's method for Dictionary-based Query Translation, using freely available dictionaries. Broad-coverage parsing and rule-based matching was used for question answering. Routing was performed using Latent Semantic Indexing in profile space.