Text REtrieval Conference (TREC)
System Description

Organization Name: Dartmouth College Run ID: Dm8Nbn
Section 1.0 System Summary and Timing
Section 1.1 System Information
Hardware Model Used for TREC Experiment: SGI Origin 2000
System Use: SHARED
Total Amount of Hard Disk Storage: 160 Gb
Total Amount of RAM: 8702 MB
Clock Rate of CPU: 250 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: 16 processors; shared memory. We didn't take especial advantage of this, but simply ran tests concurrently on multiple processors.
Section 2.0 Construction of Indices, Knowledge Bases, and Other Data Structures
Length of the stopword list: 571 words
Type of Stemming: PORTER
Controlled Vocabulary: NO
Term weighting: YES
  • Additional Comments on term weighting: see comments
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: Local term weighting based on normalization of document length. Global term weighting based on a new Information Theoretic model that we have developed.
Section 3.0 Statistics on Data Structures Built from TREC Text
Section 3.1 First Data Structure
Structure Type: OTHER DATA STRUCTURE
Type of other data structure used: database
Brief description of method using other data structure: database table of (term, doc, freq) triples
Total storage used: 2.8 Gb
Total computer time to build: 16 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:
  • 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: 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): 100 CPU seconds
Times broken down by component(s):
Section 5.1 Searching Methods
Vector space model: NO
Probabilistic model: YES
Cluster searching: NO
N-gram matching: NO
Boolean matching: NO
Fuzzy logic: NO
Free text scanning: NO
Neural networks: NO
Conceptual graphic matching: NO
Other: YES
Additional comments: Other: Information Theory-based model
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: NO
Proximity of terms: NO
Information theoretic weights: YES
Document length: NO
Percentage of query terms which match: NO
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:     TITLE   DESCRIPTION   NARRATIVE  
Average computer time to build query 0    CPU seconds
Term weighting (weights based on terms in topics): NO
Phrase extraction from topics: NO
Syntactic parsing of topics: NO
Word sense disambiguation: NO
Proper noun identification algorithm: NO
Tokenizer: NO
  • Patterns which were tokenized:
Expansion of queries using previously constructed data structures: NO
  • Comment:
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
  • 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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