Text REtrieval Conference (TREC)
System Description

Organization Name: University of Wales Bangor Run ID: uwbqitekat03
Section 1.0 System Summary and Timing
Section 1.1 System Information
Hardware Model Used for TREC Experiment: PC Cluster
System Use: SHARED
Total Amount of Hard Disk Storage: 4 Gb
Total Amount of RAM: 1024 MB
Clock Rate of CPU: 500*8 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: Synonym expansion, word sense disambiguation, inference on knowledge based relations
List of features that are present in the system, and impacted its performance, but are not detailed within this form: Distributed knowledge base. PPM named entity extraction. Automated external answer corroboration.
Section 2.0 Construction of Indices, Knowledge Bases, and Other Data Structures
Length of the stopword list: 250 words
Type of Stemming: NONE
Controlled Vocabulary: YES
Term weighting: NO
  • Additional Comments on term weighting:
Phrase discovery: YES
  • Kind of phrase:
  • Method used: OTHER
Type of Spelling Correction: NONE
Manually-Indexed Terms: YES
Proper Noun Identification: YES
Syntactic Parsing: YES
Tokenizer: YES
Word Sense Disambiguation: NO
Other technique: NO
Additional comments: Hybrid approach adopting Rule based, Statistical and PPM methods in a cascading finite state architecture.
Section 3.0 Statistics on Data Structures Built from TREC Text
Section 3.1 First Data Structure
Structure Type: KNOWLEDGE BASE
Type of other data structure used:
Brief description of method using other data structure:
Total storage used: 2 Gb
Total computer time to build: 72 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: YES
  • Number of concepts represented: 12
Type of representation: logical tuple relation
Auxilary files used: NO
  • Type of auxilary files used:
Additional comments: Question oriented 'knows' and 'knows about' relations specifying high level abstractions of knowledge. Used to adopt an agant based approach.
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 INDEPENDENT
Total Storage: 0.2 Gb
Number of Concepts Represented: 1000 concepts
Type of representation: RULES
Automatic or Manual: MANUAL
  • Total Time to Build: 144 hours
  • Total Time to Modify (if already built): 12 hours
Type of Manual Labor used: MOSTLY MANUALLY BUILT USING SPECIAL INTERFACES
Additional comments: Syntactic and Semantic rule based systems combined with regular expression, using abck substitution of named entities to auto-generate docuemnt specific rules. External text data used for PPM training.
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): <1 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: YES
Additional comments: Grid based cluster of Agents in direct communication, able to query for available knowledge (domain and context) and route questions to appropriate knowledge base.
Section 5.2 Factors in Ranking
Term frequency: YES
Inverse document frequency: NO
Other term weights: YES
Semantic closeness: NO
Position in document: NO
Syntactic clues: YES
Proximity of terms: NO
Information theoretic weights: NO
Document length: NO
Percentage of query terms which match: YES
N-gram frequency: NO
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:
Send questions to trec@nist.gov

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