Text REtrieval Conference (TREC)
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Organization Name: Macquarie University | Run ID: afrun1 |
Section 1.0 System Summary and Timing |
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Section 1.1 System Information |
Hardware Model Used for TREC Experiment:PC Intel4 2.8GHz System Use:DEDICATED Total Amount of Hard Disk Storage:80 Gb Total Amount of RAM:1 MB Clock Rate of CPU:2800 MHz |
Section 1.2 System Comparisons |
Amount of developmental "Software Engineering":SOME 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:words Type of Stemming:NONE Controlled Vocabulary: Term weighting:
Phrase discovery:
Type of Spelling Correction:NONE Manually-Indexed Terms: Proper Noun Identification: Syntactic Parsing: Tokenizer: Word Sense Disambiguation: Other technique: Additional comments:Used the preselected documents provided by NIST |
Section 3.0 Statistics on Data Structures Built from TREC Text |
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Section 3.1 First 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 3.2 Second 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 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:OTHER Domain type:DOMAIN SHARED Total Storage:.19 Gb Number of Concepts Represented:416 concepts Type of representation:RULES Automatic or Manual:AUTOMATIC
Type of Manual Labor used:NONE Additional comments:Graph rules automatically learned from a subset of 111 questions from the TREC 2004 QA track, extended with answer sentences with manually annotated answers |
File is:OTHER Total Storage:0.075 Gb Number of Concepts Represented:560 concepts Type of representation:OTHER Additional comments:A subset of 111 questions from the TREC 2004 QA track, extended with answer sentences with manually annotated answers. The total number of question/answer pairs was 560. |
Section 5.0 Computer Searching |
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Average computer time to search (per query): CPU seconds |
Times broken down by component(s): |
Section 5.1 Searching Methods |
Vector space model:NO Probabilistic model:NO Cluster searching:NO N-gram matching:NO Boolean matching:NO Fuzzy logic:NO Free text scanning:NO Neural networks:NO Conceptual graphic matching:YES Other:NO Additional comments: |
Section 5.2 Factors in Ranking |
Term frequency:YES Inverse document frequency:NO Other term weights: Semantic closeness:NO Position in document:NO Syntactic clues:YES Proximity of terms: Information theoretic weights:NO Document length:NO Percentage of query terms which match:YES N-gram frequency:NO Word specificity:NO Word sense frequency:NO Cluster distance:NO Other:NO Additional comments: |
Section 6.0 Query Construction |
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Section 6.1 Automatically Built Queries for Ad-hoc Tasks |
Topic fields used: Average computer time to build query 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: NONE |
Section 6.2 Manually Constructed Queries for Ad-hoc Tasks |
Topic fields used: Average time to build query? minutes Type of query builder: OTHER Tool used to build query: NONE Method used in intial query construction? BOOLEAN CONNECTORS 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: |
Disclaimer: Contents of this online document are not necessarily the official views of, nor endorsed by the U.S. Government, the Department of Commerce, or NIST. |