Text REtrieval Conference (TREC)
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Organization Name: LangPower Computing, Inc. | Run ID: LPC7 |
Section 1.0 System Summary and Timing |
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Section 1.1 System Information |
Hardware Model Used for TREC Experiment:PC Pentium IV. System Use:SHARED Total Amount of Hard Disk Storage:80 Gb Total Amount of RAM:1000 MB Clock Rate of CPU:3.6 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:YES
Phrase discovery:YES
Type of Spelling Correction:AUTOMATIC CORRECTION Manually-Indexed Terms:NO Proper Noun Identification:NO Syntactic Parsing:YES Tokenizer:YES Word Sense Disambiguation:NO Other technique:YES Additional comments:term weighting is article-specific and ajusted based on the number of noun-phrases. Search terms are constructed using tumor-specific term rules from a dictionary for tumor categorization task. |
Section 3.0 Statistics on Data Structures Built from TREC Text |
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Section 3.1 First Data Structure |
Structure Type:OTHER DATA STRUCTURE Type of other data structure used:phrase-based indexing structure Brief description of method using other data structure:toward the concept-oriented indexing Total storage used:1 Gb Total computer time to build:18 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:YES
Type of representation:noun phrase Auxilary files used:NO
Additional comments:cut-off for the concept represented was at 4, the frequency of the concept in the data collection. Represented concepts are automically discovered from the data collection. The method applies to all TREC experiments. |
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: 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: 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: Domain type: Total Storage:Gb Number of Concepts Represented:concepts Type of representation: Automatic or Manual:
Type of Manual Labor used: Additional comments: |
File is: Total Storage:Gb Number of Concepts Represented:concepts Type of representation: Additional comments: |
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: Probabilistic model: Cluster searching: N-gram matching: Boolean matching: Fuzzy logic: Free text scanning: Neural networks: Conceptual graphic matching: Other: Additional comments: |
Section 5.2 Factors in Ranking |
Term frequency: Inverse document frequency: Other term weights: Semantic closeness: Position in document: Syntactic clues: Proximity of terms: Information theoretic weights: Document length: Percentage of query terms which match: N-gram frequency: Word specificity: Word sense frequency: Cluster distance: Other: 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: |
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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