Text REtrieval Conference (TREC)
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Organization Name: City University, London U.K. | Run ID: city97c3 |
Section 1.0 System Summary and Timing |
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Section 1.1 System Information |
Hardware Model Used for TREC Experiment:SGI Challeng L System Use:SHARED Total Amount of Hard Disk Storage:17 Gb Total Amount of RAM:512 MB Clock Rate of CPU:150 MHz |
Section 1.2 System Comparisons |
Amount of developmental "Software Engineering":ALL List of features that are not present in the system, but would have been beneficial to have: List of features that are present in the system, and impacted its performance, but are not detailed within this form: |
Section 2.0 Construction of Indices, Knowledge Bases, and Other Data Structures |
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Length of the stopword list:0 words Type of Stemming:NONE Controlled Vocabulary:NO Term weighting:NO
Phrase discovery:NO
Type of Spelling Correction:NONE Manually-Indexed Terms:NO Proper Noun Identification:NO Syntactic Parsing:NO Tokenizer: Word Sense Disambiguation: Other technique:YES Additional comments:The other technique is Chinese word segmentation. |
Section 3.0 Statistics on Data Structures Built from TREC Text |
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Section 3.1 First Data Structure |
Structure Type:INVERTED INDEX Type of other data structure used: Brief description of method using other data structure: Total storage used:0.6333 Gb Total computer time to build:56 hours Automatic process:YES Manual hours required:hours Type of manual labor:NONE Term positions used:YES Only single terms used:NO Concepts (vs. single terms) represented:NO
Type of representation: Auxilary files used:YES
Additional comments: |
Section 3.2 Second Data Structure |
Structure Type:OTHER DATA STRUCTURE Type of other data structure used:mapping table Brief description of method using other data structure: Total storage used:3.7MB Gb Total computer time to build:1 hours Automatic process:YES Manual hours required:hours Type of manual labor:NONE Term positions used:NO Only single terms used:NO Concepts (vs. single terms) represented:NO
Type of representation: Auxilary files used:NO
Additional comments: |
Section 3.3 Third Data Structure |
Structure Type:NONE Type of other data structure used: Brief description of method using other data structure: Total storage used:Gb Total computer time to build:hours Automatic process: Manual hours required:hours Type of manual labor:NONE Term positions used: Only single terms used: Concepts (vs. single terms) represented:
Type of representation: Auxilary files used:
Additional comments: |
Section 4.0 Data Built from Sources Other than the Input Text |
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File type:LEXICON Domain type:DOMAIN INDEPENDENT Total Storage:0.61M Gb Number of Concepts Represented:70,000 concepts Type of representation:OTHER Automatic or Manual:MANUAL
Type of Manual Labor used:INITIAL CORE MANUALLY BUILT TO BOOTSTRAP FOR MACHINE-BUILT COMPLETION Additional comments:The internally-built auxiliary file is Chinese word segmentation lexicon. The total storage is 0.61MB. |
File is:NONE Total Storage:Gb Number of Concepts Represented:concepts Type of representation:NONE Additional comments: |
Section 5.0 Computer Searching |
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Average computer time to search (per query): 1689 CPU seconds |
Times broken down by component(s): Not known, but most of the time goes on accessing invert file, sorting and weighting |
Section 5.1 Searching Methods |
Vector space model:NO Probabilistic model:YES Cluster searching:NO N-gram matching:NO Boolean matching:NO Fuzzy logic:NO Free text scanning:NO Neural networks:NO Conceptual graphic matching:NO Other:NO Additional comments: |
Section 5.2 Factors in Ranking |
Term frequency:YES Inverse document frequency:YES Other term weights:NO Semantic closeness:NO Position in document:NO Syntactic clues:NO Proximity of terms:NO Information theoretic weights:NO Document length:YES Percentage of query terms which match:NO N-gram frequency:NO Word specificity:NO Word sense frequency:NO Cluster distance:NO Other:YES Additional comments:within-document term frequency, within-query term frequency, the average document length, the average relevant document length in TREC-5 and the number of query terms. |
Section 6.0 Query Construction |
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Section 6.1 Automatically Built Queries for Ad-hoc Tasks |
Topic fields used: TITLE DESCRIPTION NARRATIVE Average computer time to build query 162 CPU seconds Term weighting (weights based on terms in topics): Phrase extraction from topics: Syntactic parsing of topics: Word sense disambiguation: Proper noun identification algorithm: Tokenizer: Expansion of queries using previously constructed data structures: Automatic addition of: |
Section 6.2 Manually Constructed Queries for Ad-hoc Tasks |
Topic fields used: Average time to build query? minutes Type of query builder: Tool used to build query: Method used in intial query construction? Total CPU time for all iterations: seconds Clock time from initial construction of query to completion of final query: minutes Average number of iterations: Average number of documents examined per iteration: Minimum number of iterations: Maximum number of iterations: The end of an iteration is determined by: Automatic term reweighting from relevant documents: Automatic query expansion from relevant documents: Other automatic methods: Manual methods used: |
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