Strzalkowski, Tomek and Stein, Gees and Wise, G. Bowden and Perez-Carballo, Jose and Tapanainen, Pasi and Järvinen, Timo and Voutilainen, Atro and Karlgren, Jussi (1998) Natural language information retrieval: TREC-7 report. In: Seventh Text REtrieval Conference (TREC-7), November 1998, Gaithersburg, Maryland.
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Official URL: http://trec.nist.gov
The GE/Rutgers/SICS/Helsinki team has performed runs in the main ad-hoc task. All submissions are NLP-assisted retrieval. We used two retrieval engines: SMART and InQuery built into the stream model architecture where each stream represents an alternative text indexing method. The processing of TREC data was performed at Helsinki using the commercial Functional Dependency Grammar (FDG) text processing toolkit. Six linguistic streams have been produced, described below. Processed text streams were sent via ftp to Rutgers for indexing. Indexing was done using Inquery system. Additionally, 4 steams produced by GE NLToolset for TREC-6 were reused in SMART indexing. Adhoc topics were processed at GE using both automatic and manual topic expansion. We used the interactive Query Expansion Tool to expand topics with automatically generated summaries of top 30 documents retrieved by the original topic. Manual intervention was restricted to accept/reject decisions on summaries. We observed time limit of 10 minutes per topic. Automatic topics expansion was done by replacing human summary selection by an automatic procedure, which accepted only the summaries that obtained sufficiently high scores. Two sets of expanded topics (automatic and manual) were sent to Helsinki for NL processing, and then on to Rutgers for retrieval. Rankings were obtained from each stream index and then merged using a combined strategy developed at GE and SICS. The overall architecture of TREC-5 system has also changed in a number of ways from TREC-4. The most notable new feature is the stream architecture in which several independent, parallel indexes are built for a given collection, each index reflecting a different representation strategy for text documents. Stream indexes are built using a mixture of different indexing approaches, term extracting, and weighting strategies. We used both SMART and Prise base indexing engines, and selected optimal term weighting strategies for each stream, based on a training collection of approximately 500 MBytes. The final results are produced by a merging procedure that combines ranked list of documents obtained by searching all stream indexes with appropriately preprocessed queries. This allows for an effective combination of alternative retrieval and filtering methods, creating into a meta-search where the contribution of each stream can be optimized through training.
|Item Type:||Conference or Workshop Item (Paper)|
|Deposited By:||SICS Adminstrator|
|Deposited On:||10 Jul 2009|
|Last Modified:||18 Nov 2009 16:14|
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