Text REtrieval Conference (TREC)
System Description

Organization Name: University of Twente Run ID: utwenteB111
Section 1.0 System Summary and Timing
Section 1.1 System Information
Hardware Model Used for TREC Experiment: i386
System Use: DEDICATED
Total Amount of Hard Disk Storage: 80 Gb
Total Amount of RAM: 512 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
Length of the stopword list: 0 words
Type of Stemming: NONE
Controlled Vocabulary: NO
Term weighting:
  • Additional Comments on term 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: NO
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: INVERTED INDEX
Type of other data structure used:
Brief description of method using other data structure:
Total storage used: 1.9 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: YES
Concepts (vs. single terms) represented: NO
  • Number of concepts represented:
Type of representation:
Auxilary files used: NO
  • Type of auxilary files used:
Additional comments:
Section 3.2 Second 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: 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:
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: OTHER
Domain type: DOMAIN SHARED
Total Storage: 0.005 Gb
Number of Concepts Represented: 15 concepts
Type of representation: OTHER
Automatic or Manual: MANUAL
  • Total Time to Build: 40 hours
  • Total Time to Modify (if already built): hours
Type of Manual Labor used: INITIAL CORE MANUALLY BUILT TO BOOTSTRAP FOR MACHINE-BUILT COMPLETION
Additional comments: the auxilliary file contains the concept language models described in more detail in our paper
Externally-built Auxiliary File

File is: NONE
Total Storage: Gb
Number of Concepts Represented: concepts
Type of representation:
Additional comments:
Section 5.0 Computer Searching
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
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:
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?
  • If yes, what was the source of terms?
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:
  • Type of automatic query expansion:
Other automatic methods:
  • Other automatic methods included:
Manual methods used:
  • Type of manual method used:
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