Text REtrieval Conference (TREC)
System Description

Organization Name: Université d'Avignon Run ID: LiaIIcAuto
Section 1.0 System Summary and Timing
Section 1.1 System Information
Hardware Model Used for TREC Experiment: LINUX on 2xQuad-Core
System Use: SHARED
Total Amount of Hard Disk Storage: 146 Gb
Total Amount of RAM: 4000 MB
Clock Rate of CPU: 1860 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: 1000 words
Type of Stemming: MORPHOLOGICAL
Controlled Vocabulary: YES
Term weighting: YES
  • Additional Comments on term weighting: depending on the used ontology
Phrase discovery: YES
  • Kind of phrase: Noun phrases
  • Method used: SYNTACTIC
Type of Spelling Correction: AUTOMATIC CORRECTION
Manually-Indexed Terms: NO
Proper Noun Identification: YES
Syntactic Parsing: NO
Tokenizer: NO
Word Sense Disambiguation: YES
Other technique: YES
Additional comments: We have used Lemur Indri index and Self Organized Maps.
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: 3 Gb
Total computer time to build: 2 hours
Automatic process: NO
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: bag of words
Auxilary files used: NO
  • Type of auxilary files used:
Additional comments: We have used Lemur Indri index.
Section 3.2 Second Data Structure
Structure Type: KNOWLEDGE BASE
Type of other data structure used: Self Organised Map
Brief description of method using other data structure:indexing based on a general language ontology
Total storage used: 10 Gb
Total computer time to build: 12 hours
Automatic process: YES
Manual hours required: hours
Type of manual labor: NONE
Term positions used: YES
Only single terms used: NO
Concepts (vs. single terms) represented: YES
  • Number of concepts represented: 200 000
Type of representation: ontology
Auxilary files used: NO
  • Type of auxilary files used:
Additional comments:
Section 3.3 Third Data Structure
Structure Type: NONE
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): 3 CPU seconds
Times broken down by component(s):
Section 5.1 Searching Methods
Vector space model: YES
Probabilistic model: NO
Cluster searching: YES
N-gram matching: NO
Boolean matching: YES
Fuzzy logic: NO
Free text scanning: YES
Neural networks: YES
Conceptual graphic matching: NO
Other: YES
Additional comments:
Section 5.2 Factors in Ranking
Term frequency: YES
Inverse document frequency: YES
Other term weights: YES
Semantic closeness: YES
Position in document: YES
Syntactic clues: YES
Proximity of terms: YES
Information theoretic weights: NO
Document length: NO
Percentage of query terms which match: NO
N-gram frequency: NO
Word specificity: YES
Word sense frequency: YES
Cluster distance: YES
Other: NO
Additional comments:
Section 6.0 Query Construction
Section 6.1 Automatically Built Queries for Ad-hoc Tasks
Topic fields used:     TITLE      
Average computer time to build query 0    CPU seconds
Term weighting (weights based on terms in topics): YES
Phrase extraction from topics: YES
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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