Text REtrieval Conference (TREC)
System Description

Organization Name: GE/Rutgers Run ID: gerua1
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: NPs, head+modifier
  • Method used: SYNTACTIC
Type of Spelling Correction: NONE
Manually-Indexed Terms: NO
Proper Noun Identification: YES
Syntactic Parsing: YES
Tokenizer: YES
Word Sense Disambiguation: NO
Other technique: YES
Additional comments: Syntactic normalization for head+modifier pairs in order to cover a variety of syntactic forms with equivalent semantics. These include: noun+adjective, noun+noun, noun+prep+noun, verb+object, and subject+verb syntactic patterns.
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 within a stream
Auxilary files used:
  • Type of auxilary files used:
Additional comments: The index is organized into the structure of streams, i.e., parallel indexes with alternative representations of the data. Internally, only single terms are used but within each stream they represent different entities: words, phrases, head+modifier concepts, proper names, etc.
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:
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: YES
Inverse document frequency: YES
Other term weights: NO
Semantic closeness: NO
Position in document: NO
Syntactic clues: NO
Proximity of terms: NO
Information theoretic weights: NO
Document length: YES
Percentage of query terms which match: NO
N-gram frequency: NO
Word specificity: NO
Word sense frequency: NO
Cluster distance: NO
Other: YES
Additional comments: Retrieval was peformed in parallel by each stream, and results were merged. The final document rank was affected by the
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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