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Lundquist, C.; Frieder, O.; Holmes, D.O.; Grossman, D.: ¬A parallel relational database management system approach to relevance feedback in information retrieval (1999)
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- Date
- 17. 1.2000 12:22:18
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Aljlayl, M.; Frieder, O.; Grossman, D.: On bidirectional English-Arabic search (2002)
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- Abstract
- Aljlayl, Frieder, and Grossman review machine translation of query methodologies and apply them to English-Arabic/Arabic-English Cross-Language Information Retrieval. In the dictionary method, replacement of each term with all possible equivalents in the target language results in considerable ambiguity, while taking the first term in the dictionary list reduces the ambiguity but may fail to capture the meaning. A Two-Phase method takes all possible equivalents and translates them back, retaining only those that generate the original term. It results in an average query length of six terms in TREC7 and 12 in TREC9. Arabic to English translations consistently preformed below the original English queries, and the Two-Phase method consistently preformed at the highest level and significantly better than the Every-Match method. Machine translation using other techniques is economical for queries but not likely so for documents. Using ALKAFI, a commercial translation system from Arabic to English and the Al-Mutarjim Al-Arabey system for English to Arabic, nearly 60% of monolingual retrievals were generated going from Arabic to English. Smaller numbers of terms in the source query improve performance, and these systems require syntactically well-formed queries for good performance.
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Aqeel, S.U.; Beitzel, S.M.; Jensen, E.C.; Grossman, D.; Frieder, O.: On the development of name search techniques for Arabic (2006)
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- Date
- 22. 7.2006 17:20:20
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Cathey, R.J.; Jensen, E.C.; Beitzel, S.M.; Frieder, O.; Grossman, D.: Exploiting parallelism to support scalable hierarchical clustering (2007)
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- Abstract
- A distributed memory parallel version of the group average hierarchical agglomerative clustering algorithm is proposed to enable scaling the document clustering problem to large collections. Using standard message passing operations reduces interprocess communication while maintaining efficient load balancing. In a series of experiments using a subset of a standard Text REtrieval Conference (TREC) test collection, our parallel hierarchical clustering algorithm is shown to be scalable in terms of processors efficiently used and the collection size. Results show that our algorithm performs close to the expected O(n**2/p) time on p processors rather than the worst-case O(n**3/p) time. Furthermore, the O(n**2/p) memory complexity per node allows larger collections to be clustered as the number of nodes increases. While partitioning algorithms such as k-means are trivially parallelizable, our results confirm those of other studies which showed that hierarchical algorithms produce significantly tighter clusters in the document clustering task. Finally, we show how our parallel hierarchical agglomerative clustering algorithm can be used as the clustering subroutine for a parallel version of the buckshot algorithm to cluster the complete TREC collection at near theoretical runtime expectations.
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Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Grossman, D.; Frieder, O; Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system (2004)
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Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Frieder, O.; Grossman, D.: Temporal analysis of a very large topically categorized Web query log (2007)
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