Text REtrieval Conference (TREC)
System Description

Organization Name: Concordia University Run ID: BioKI1
Section 1.0 System Summary and Timing
Section 1.1 System Information
Hardware Model Used for TREC Experiment:
System Use: DEDICATED
Total Amount of Hard Disk Storage: 800 Gb
Total Amount of RAM: 8000 MB
Clock Rate of CPU: 2000 MHz
Section 1.2 System Comparisons
Amount of developmental "Software Engineering": NONE
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
Length of the stopword list: words
Type of Stemming: PORTER
Controlled Vocabulary: NO
Term weighting: YES
  • Additional Comments on term weighting: manual weighting
Phrase discovery: NO
  • Kind of phrase:
  • Method used: OTHER
Type of Spelling Correction: NONE
Manually-Indexed Terms: NO
Proper Noun Identification: NO
Syntactic Parsing: NO
Tokenizer: YES
Word Sense Disambiguation: NO
Other technique: NO
Additional comments:
Section 3.0 Statistics on Data Structures Built from TREC Text
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:
  • Number of concepts represented:
Type of representation:
Auxilary files used:
  • Type of 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:
  • Number of concepts represented:
Type of representation:
Auxilary files used:
  • Type of 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:
  • Number of concepts represented:
Type of representation:
Auxilary files used:
  • Type of auxilary files used:
Additional comments:
Section 4.0 Data Built from Sources Other than the Input Text
Internally-built Auxiliary File

File type: NONE
Domain type: DOMAIN INDEPENDENT
Total Storage: Gb
Number of Concepts Represented: concepts
Type of representation: NONE
Automatic or Manual:
  • Total Time to Build: hours
  • Total Time to Modify (if already built): hours
Type of Manual Labor used: NONE
Additional comments:
Externally-built Auxiliary File

File is: NONE
Total Storage: Gb
Number of Concepts Represented: concepts
Type of representation: NONE
Additional comments:
Section 5.0 Computer Searching
Average computer time to search (per query): 7200 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: YES
Neural networks: NO
Conceptual graphic matching: NO
Other: NO
Additional comments:
Section 5.2 Factors in Ranking
Term frequency: NO
Inverse document frequency: NO
Other term weights: YES
Semantic closeness: NO
Position in document: NO
Syntactic clues: NO
Proximity of terms: YES
Information theoretic weights: NO
Document length: YES
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
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:
  • Patterns which were tokenized:
Expansion of queries using previously constructed data structures:
  • Comment:
Automatic addition of: NONE
Section 6.2 Manually Constructed Queries for Ad-hoc Tasks
Topic fields used:         OTHER
Average time to build query? 2   minutes
Type of query builder: SYSTEM EXPERT
Tool used to build query: NONE
Method used in intial query construction? TERM WEIGHTING
  • If yes, what was the source of terms?
Total CPU time for all iterations: 2800  seconds
Clock time from initial construction of query to completion of final query: 45   minutes
Average number of iterations: 2
Average number of documents examined per iteration: 3
Minimum number of iterations: 1
Maximum number of iterations: 4
The end of an iteration is determined by: individual judgment
Automatic term reweighting from relevant documents: NO
Automatic query expansion from relevant documents: NO
  • Type of automatic query expansion: ALL TERMS IN \
Other automatic methods: NO
  • Other automatic methods included:
Manual methods used: YES
  • Type of manual method used: USING INDIVIDUAL JUDGMENT (NO SET ALGORITHM)
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