INDEX

Additive conjoint structure, 120, 121
Additive independence, 121
Annual Review of Information Scienceand Technology, v, 10, 12
Association measures, see Measures ofassociation
Attributes, 34
Attribute-value pairs, 56
Automatic abstracting, 14
Automatic classification, 2, 8, 29-55,
133-134
Automatic content analysis, 2, 6, 7,
136-137
Automatic document classification, seeDocument clustering
Automatic indexing, 21,26
Automatic keyword clustering, seeKeyword classification
Automatic text analysis, 3, 12-28
Automatic thesaurus, see Keyword
classification
Averaging techniques, 101-103
Balanced tree, 72
Binary tree, 72Boolean search, 59, 60, 81-83
Bottom-up search, 89
Bump table, 76
Cellular multi-lists, 63-65
Centroid, see Cluster representative
Citation graph clustering, 52
Classification (definition), 29
Classification algorithms, 39, 42-50
Classification methods, 34-36, 38-45
Classification programs, 51

Clique, see Maximal complete subgraph
Clumps, 36, 42
Cluster hypothesis, 37-38
Cluster methods, see Classification
methods
Cluster profile, see Cluster representa­
tive
Cluster representative, 42, 43, 44, 48,
85-87
Cluster time dependence, 49-50
Cluster-based retrieval, 38, 47, 87-89
Clustered file, 8, 78
Clustering, see Classification methods,
Classification algorithms
Coarse clustering, 49
Collision handling methods, 75-86
Combination function, 1 24
Common words, see Stop words
Composite measures, 105Computer-aided instruction, 138
Conflation, 15-20
Conflation algorithm, 20
Connected component, 40, 46
Connectedness, 118
Connection matrix, 46, 47
Content sensitive suffix removal, 15Contingency table, 99
Control parameter, 101
Controlled vocabulary, 2 1 ,2 4
Co-occurrence, 7, 24, 25
Co-ordination level, 83
Core custering, 49
Correlation measure, see Measures ofassociation
Cosine coefficient, 32, 34
Cosine correlation, 84

149

INDEX
Cranfield II, 10
Cumulative frequency
126-127

distribution,

Data retrieval systems, 1,138
Data simplification, 35
Data structure, see File structures
Decision rule, 88
Decomposable structure, 121
Decreasing marginal effectiveness, 122
Degenerate trees, 72-73
Dendrogram, 45, 65, 71
Diagnosis (definition), 29
Dice’s coefficient, 32
Directory, 58
Dissimilarity coefficient, 33, 45, 46
Dissimilarity matrix, 46
Document clustering, 8, 30, 36, 38,
47-50
Document frequency weighting, 23
Document representative, 5, 13, 15,20, 25
Doubly chained tree, 68-70
Dynamic classification, 48-49
Zf-measure, 324
Effectiveness, 2, 9, 96, see also
Measurement of effectiveness
Efficiency, 2, 9
Empirical distribution function, 126
Empirical ordering, 117
Essential component, 120
Evaluation, 1 ,3 ,8 , 95-132, 135
Exhaustivity, see Indexing exhaustivity
Expected search length, 109-113
Experimental information retrieval, 1,2,48
Fallout, 100, 106
Feedback, 6, 90-93
Field, 57File organisation, see File structures
File structures, 3, 56-80, 134
Fixed increment error correction, 91
Formal semantics, 13
Frequency of occurrence, 7, 13, 14,23, 24
Fuzzy sets, 52Generality, 100
Graph theoretic
39-41

150

clustermethods,

Hash addressing, 74-87
Hashing function, 75-76
Heuristic cluster methods, 42-44
Hierarchic classification, 44-48
Hierarchic cluster methods, 41-48
High-frequency words, see Stop words
Independence, 119
Index language, 20-21
Index language specificity, 22
Index term, 20
Index term weighting, 22-24
Indexing, 4, 20-22
Indexing exhaustivity, 22-23
Indexing specificity, 22-23
Index-sequential file, 60-63
Information retrieval (definition), 1
Information retrieval system, 1, 5
Information structure, 6, 7Interaction term, 121
Interactive search formulation, 89-90
Interpolation, 103-104
Intersubstitutibility,see
Keywordsubstitution
Inverse document frequency weighting,
23
Inverted file, 8, 59-60
Jaccard’s coefficient, 32
AMist, 58
K-pointer, 58
Key, 58
Keyword, 7, 20
Keyword, classes, 25, 41
Keyword classification, 24-25, 38, 41
Keyword co-occurrence, see Co­occurrence
Keyword frequency, see Frequency ofoccurrence
Keyword substitution, 25
Linguistics, 12, 13
Log precision, 116
Logical relevance, 98
Low-frequency words, see Rare words
Macro-evaluation, 101-104
Matching function, 42, 43, 83-85
Maximal complete subgraph, 40
Maximally linked document, 86
Measurement of effectiveness, 2, 8,99-130
Measures o f association, 7, 31-34

INDEX
Measures of effectiveness, 8, 9, 99-101
MEDLARS, 21, 2 4 ,9 0
MEDLINE, 6, 90
MEDUSA, 6, 90
Micro-evaluation, 101-102
Mini-computer, 1 38
Minimal premiss set, 98
Monothetic, 35
Multi-level classification, 43
Multi-lists, 63Natural classification, 30
Nearest neighbour classification, seeSingle-link
Negative dictionary, see Stop listNetworks, 138
Non-parametric tests, 128-129
Normalisation of text, 26
Normalised association measures, 32
Normalised precision, 114-115
Normalised recall,113-114,115
Normalised symmetric difference, 33,34, 116-117, 124
Numerical relational structure, 121
Numerical representation, 121-122
Operating characteristic, 107-108
Operational information retrieval, 1 ,6 ,48
Optimal query, 92
Order dependence, 44
Order independence, 39
Ordered classification, 36
Overlap coefficient, 32
Pointer, 57
Polythetic, 35
Post-coordination, 20
Precision, 8, 9, 22, 97, 99-101, 106
Precision-recall curve, 100
Pre-coordination, 20
Presentation of experimental results,
125-127
Probabilistic indexing, 26'Query representative, see Document
representative
Question-answering systems, 1, 98,
137
Rag-bag cluster, 43, 44
Rank recall, 116
Rare words, 15
Recall, 8 ,9 ,2 2 , 97, 9 9 -1 01 ,10 6

Reclassification, 49
Record, 56
Relational structure, 118
Relative importance of recall andprecision, 1 23
Relevance, 4, 5, 8, 97-99
Relevance feedback, 90-93
Representation theorem, 120-122
Restricted solvability, 120
Retrieval effectiveness, see Measures ofeffectiveness
Ring structure, 65-67
Rocchio algorithm, 43
Scale, 121
Scatter storage, 74-77
Search strategies, 3, 81-94, 135
Sequential file, 7, 59
Serial search, 38, 84
Sign test, 128-129
Significance tests, 8, 127-129
Similarity coefficients, 32
Similarity matrix, 40
Similarity matrix generation, 50
Simple matching coefficient, 32, 83
Simple ordering, 110
Simulation, 135Single-link, 4 1 ,4 5 -4 7Single-link algorithm, 46, 47
Single-pass algorithm, 43
SMART, v, 21, 30, 51
SNOB, 50
Specificity,
see
Index
language
specificity, Indexing specificity
Specificity weighting, 23
Stability of classification, 39
Standard recall values, 101
Statistical significance, see Significancetests
Stemming, 15-20
Stop list, 15, 16-17*
Stop words, 14, 15-17
Stopping rule, 88
Stratified hierarchic cluster methods,
47-48
Suffix stripping, 15-20
Suffixes, 15, 18-19
Swets model, 105-109
Term, see Keyword
Term classes, see Keyword classes
Term clustering, see Keyword classi­
fication
Term co-occurrence, see Co-occurrence

151INDEX
Term frequency, see Frequency ofoccurrenceTerm substitution, see Keyword
substitution
Terminal node, 71
Theoretical soundness of classification,
39
Theory of measurement, 117
Thesaurus, 24
Thomsen condition, 120, 125
Threaded list, 67-70
Top-down search, 89
Trainable pattern classifier, 91
Transitivity, 118
Trees, 70-74

152

Triangle inequality, 33
Typical document, 86
Uncontrolled vocabulary, 21
Uniqueness of single-link, 48
Updating classifications, 49-50
Weak ordering, 110, 118
Weighting schemes, 23
Wilcoxon matched pairs test, 128
Word frequency, see Frequency ofoccurrence
Zipfian distribution, 13-14, 23, 26
Zipf’slaw, 13-14