Text REtrieval Conference (TREC)
System Description

Organization Name: GE Run ID: genlp1
Section 1.0 System Summary and Timing
Section 1.1 System Information
Hardware Model Used for TREC Experiment: Sun Sparc Enterprise
System Use: SHARED
Total Amount of Hard Disk Storage: 20 Gb
Total Amount of RAM: 256 MB
Clock Rate of CPU: 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: 300 words
Type of Stemming: MORPHOLOGICAL
Controlled Vocabulary: NO
Term weighting: YES
  • Additional Comments on term weighting: SMART tf*idf
Phrase discovery: YES
  • Kind of phrase: NP, head+modifier
  • Method used: SYNTACTIC
Type of Spelling Correction: NONE
Manually-Indexed Terms: NO
Proper Noun Identification: YES
Syntactic Parsing: YES
Tokenizer: NO
Word Sense Disambiguation: NO
Other technique: YES
Additional comments: Syntactic normalization to capture head+modifier pairs covering a variety of syntactic constructs with equivalent semantics. Patterns covered include: noun+noun, noun+adjective, noun+prep+noun, verb+object, and subject+verb.
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: 8 Gb
Total computer time to build: 5 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: metaterms, many
Type of representation: terms in streams
Auxilary files used: NO
  • Type of auxilary files used:
Additional comments: The index is organized into a structure of streams, which are parallel representations of the data.
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): 4.0 CPU seconds
Times broken down by component(s): 1.0 sec/stream
Section 5.1 Searching Methods
Vector space model: YES
Probabilistic model: NO
Cluster searching: NO
N-gram matching: NO
Boolean matching: NO
Fuzzy logic: NO
Free text scanning: NO
Neural networks: NO
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:
Send questions to trec@nist.gov

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