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● 1
● 2 human indexing versus automatic indexing
● 3 results of human indexing versus automatic indexing
● 4 multiple approaches to indexing in IR databases
● 5 automatic indexing of language texts versus image texts and other non-language texts
● 6 recommended resources on indexing processes
8.1. Research Comparing Automatic and Human Indexing.
● 7
● 8 role of users in IR research
● 9 variables in IR research
● 10 size of documentary units among variables in IR research
● 11 extent of indexable matter among variables in IR research
● 12 exhaustivity among variables in IR research
● 12a specificity among variables in IR research
● 13 browsability among variables in IR research
● 14 syntax among variables in IR research
● 15
● 16 vocabulary management among variables in IR research
● 17 surrogation among variables in IR research
● 18 conflation of variables in IR research
● 19 views of Cooper (William S.) on variables in IR research
● 20 conflation of variables in IR research
● 21 role of users in IR research at TREC
● 22 evidence from use of automatic indexing versus human indexing
● 23 user preferences for automatic indexing versus human indexing
● 24 effectiveness of automatic indexing
● 25 cost-benefit analysis of human indexing versus automatic indexing
● 26
8.2. Human Analysis for Indexing.
● 27 methods of human analysis for human indexing
● 28 cognitive processes in human indexing
● 29
● 30 role of documentary features in human indexing
● 31 cognitive processes in human indexing
● 32 analysis steps in human indexing
● 33 views of Mulvany (Nancy) on human indexing
● 34
● 35
● 36
● 37 cultural factors in human indexing versus automatic indexing
● 38 cultural factors in automatic indexing
● 39 views of Chan (Lois Mai) on human indexing
● 40
● 41
● 42 views of Chicago manual of style on human indexing
● 43 views of Fugmann (Robert) on human indexing
● 44 views of Soergel (Dagobert) on human indexing
● 45 views of Lancaster (F. W.) on human indexing
● 46 views of Fairthorne (Robert) on human indexing
● 47 views of O'Connor (Brian) on human indexing
● 48 views of Wellisch (Hans) on human indexing
● 49
● 50
● 51 views of Wilson (Patrick) on human indexing
● 52
● 53
● 54
● 55 concrete entity and event databases versus IR databases
● 56 views of Taylor (Arlene) on human indexing
● 57 views of Hjørland (Birger) on human indexing
● 58 activity theory: treatment of knowledge organization
● 59 paradigms of information science
● 60 role of domain analysis in information understanding
● 61 views of Hjørland (Birger) on nature of subjects
● 62
● 63 variability in human indexing
● 64 consistency in human indexing
● 65
● 66 inconsistency in searching
8.2.1. Cognition Versus Social Construction in Human Analysis and Indexing.
● 67 views of Frohmann (Bernd) on human indexing
● 68
● 69 views of Foskett (A. C.) on human indexing
● 70 views of Farradane (Jason) on human indexing
● 71
● 72 views of Beghtol (Clare) on human indexing
● 73 views of Anderson (James D.) on human indexing
● 74
● 75 views of Artandi (Susan) on human indexing
● 76 human indexing as model for automatic indexing
● 77 positive attributes of human indexing
● 78 application of views of Wittgenstein (Ludwig) to human indexing
● 79
● 80 application of views of Wittgenstein (Ludwig) to social construction of indexing rules
● 81 queer theory compared to indexing theory
● 82 queer theory
● 83 essentialism versus social constructionism in gender studies
● 84
● 85 role of gender in human indexing
● 86 social construction of gender
● 87 culture versus cognition in human indexing
● 88 views of Frohmann (Bernd) on social context of human indexing
● 89
8.2.2. Human Indexing Rules.
● 90 human indexing as two step process
● 91 rules for analysis in human indexing
● 92 standards for analysis in human indexing: British and international
● 93 guidelines for analysis in cataloging and classification at Rutgers University
● 94 subjective nature of guidelines for indexing
● 95 views of Hjørland (Birger) on guidelines for indexing
● 96 relation of subject scope and documentary scope to rules for human indexing
● 97 specialized rules for human indexing
● 98 rules for indexing for MLA international bibliography
● 99 rules for indexing about diesel engines by Ranganathan
● 100 role of specialized categories in human indexing
● 101 limitations of rules for human indexing
● 102 qualitative judgments in request-oriented human indexing
● 103 views of Frohmann (Bernd) on rules for human indexing
● 104 purposes of information retrieval for diverse users
● 105 domain analysis as basis for rules for human indexing
● 106 wants versus needs in information retrieval
● 107 political aspects of information retrieval
● 108
identification of non-topical features in human indexing;
bibliographic coupling and co-citation as basis for indexing
● 109
8.2.2.1. Human Indexing Rules for Image Text.
● 110 views of Jorgensen (Corinne) on indexing of image texts
● 111 views of Pérez-López (Kathleen Golitko) on automatic indexing of image texts
● 112 recommended resources on human indexing of image texts
● 113 terminology for image texts and sound texts
8.2.2.2. Human Indexing Rules Based on Probabilistic Analysis.
● 114 views of Frohmann (Bernd) on rules for human indexing of Cooper (William S.)
● 115 views of Cooper (William S.) on human indexing
● 116 decision theory, utility theory, and gedanken experimentation in rules for human indexing
● 117
● 118
● 119 odds-payoff indexing chart
Figure 8.1. Cooper's odds-payoff indexing chart
"Possible format for a graphic aid to gedanken indexers.
The data are fictitious" (Cooper 1978, p. 117)
● 120
● 121
● 122
● 123
● 124 numerical values for decision making in human indexing
8.3. Automatic Indexing.
● 125 automatic indexing versus human searching
● 126 automatic indexing of language texts versus image texts and sound texts
● 127 indexing of image texts by Altavista web search engine
● 128 theoretical models for automatic indexing: vector-space model, probabilistic model
● 129 language model for automatic indexing
● 130 recommended resources on automatic indexing
8.3.1. In the Beginning Was the Word.
● 131 definitions of words in automatic indexing
● 132 definitions of words in Chinese language
● 133 treatment of punctuation in automatic indexing
● 134 treatment of hyphens in automatic indexing
● 135 treatment of slashes in automatic indexing
● 136 treatment of underscores and full stops (periods) in automatic indexing
● 137 treatment of parentheses in automatic indexing
● 138 treatment of apostrophes in automatic indexing
● 139 treatment of numbers in automatic indexing
● 140
● 141
● 142 treatment of single characters in automatic indexing
● 143 definition of words in automatic indexing
● 144 treatment of upper- and lower-case letters in automatic indexing
8.3.2. Simple Keyword Indexing.
● 145
8.3.3. Negative Vocabulary Control: Stop Lists.
● 146 stop lists for reducing size of indexes
● 147 choice of words for stop lists
● 148 number of words in stop lists
● 149 negative vocabulary control
8.3.4. Counting Words.
● 150 use of frequency of words for ranking texts
8.3.5. Comparative Counting and Weighting.
● 151 inverse document frequency of words
● 152 calculation of document weights
● 153
● 154
8.3.6. Improving the Count: Stemming.
● 155 impact of stemming on frequency of words
● 156 identification of word roots in stemming
● 157 stemming of plural "s" suffixes
● 158 stemming of multiple suffixes
● 159 impact of stemming
8.3.7. Natural Word Distributions.
● 160 Zipf's law on distributions of words in texts
● 161 identification of keywords based on transition points in Zipfian distributions
● 162 automatic indexing compared to human indexing
● 163 Zipfian distribution of words in article by Booth (A. D.)
● 164 transition point in Zipfian distribution of words
● 165 identification of keywords based on transition points in Zipfian distributions
● 166 effectiveness of keywords
● 167 keywords based on Zipfian distributions compared to human indexing
● 168 incompatibility of human indexing compared to automatic indexing
● 169 automatic indexing compared to human indexing
● 170
● 171
● 172
● 173
● 174
● 175
● 176
● 177
8.3.8. Words Versus Phrases.
● 178 importance of phrases in automatic indexing
● 179 proper nouns in indexing
● 180 cost versus benefits in identification of phrases in automatic indexing
● 181 identification of phrases in automatic indexing and in searching
● 182
● 183
● 184
● 185 identification of phrases in automatic indexing
● 186 role of phrases in browsing
● 187
● 188
● 189
8.3.9. Managing Vocabulary in Automatic Indexing.
● 190
● 191 positive vocabulary management in automatic indexing
● 192 vocabulary management of equivalent and synonymous terms
● 193 vocabulary management of minor terms
● 194 vocabulary management in automatic indexing
● 195 vocabulary management for displayed indexes
● 196 vocabulary management for electronic searching
● 197 addition of terms to thesauri in automatic indexing
● 198 bypassing vocabulary management in electronic searching
8.3.10. Automatic Vocabulary Management.
● 199
● 200 Associative Interactive Dictionary as example of automatic vocabulary management
● 201 identification of related terms by co-occurrence
● 202 ranking of related terms by frequency of co-occurrence
● 203
● 204
● 205
● 206
● 207
● 208 impact of automatic vocabulary management
8.3.11. Clustering.
● 209 definitions of classing and clustering
● 210 criteria for clusters
● 211 clusters in searching
● 212 document similarity as basis for clustering
● 213 types of clusters: string clusters
● 214 star clusters
● 215 clique clusters
● 216 clump clusters
Figure 8.2. Types of clusters, based on Salton (1975a).
● 217 thresholds in automatic clustering
● 218 automatic clustering techniques: static clustering, dynamic clustering, scatter-gather clustering
● 219 static clustering
● 220
8.3.11.1. Latent Semantic Indexing.
● 221
● 222 vocabulary management in latent semantic indexing
8.3.12. Citation Indexes.
● 223 citation links to older documents
● 224 citation indexes to newer documents
8.3.12.1. Bibliographic Coupling.
● 225 definition of bibliographic coupling
● 226 bibliographic coupling compared to co-citation
8.3.12.2. Co-Citation.
● 227 definition of co-citation; identification of research fronts by co-citation
8.3.13. Relevance Feedback.
● 228 feedback in automatic indexing and in searching
● 229 purpose of relevance feedback
● 230
● 231 procedures in relevance feedback
● 232
● 233 relevance feedback in selective dissemination of information and filtering
● 234 role of human searching behavior in automatic indexing
● 235 pseudo relevance feedback
8.4. Subject Analysis and Indexing in Indexing and Abstracting Services.
● 236
● 237
● 238 MedIndEx as example of expert system for subject analysis and indexing
● 239 use of checktags in subject analysis and indexing
● 240 computer-aided subject analysis and indexing for indexing and abstracting services
8.5. Growing Role of Automatic Analysis and Indexing.
● 241 allocation of automatic indexing versus human indexing
● 242 allocation of human indexing to important documents
● 243
● 244 use of human indexing for identification of useful documents
● 245 views of Bates (Marcia J.) on role of human indexing
● 246
● 247 criteria for allocation of human indexing
8.5.1. Censorship or Guidance?
● 248 measures of use versus censorship
● 249 expert judgment versus use in evaluation of importance
● 250 selection of useful documents by advisory groups and indexing staff
● 251 expert judgment versus user preferences in IR database design
● 252 expert judgment in indexing
● 253 role of human indexers in assessments of authority
● 254 identification of contributions by human indexers
● 255 discovery of controversial documents
● 256 inequality of documents
● 257 application of expert judgment to world-wide web and internet
● 258 machines versus humans in indexing
8.6. Our Examples.
8.6.1. A Book Index.
● 259
8.6.2. An Indexing and Abstracting Service.
● 260
8.6.3. A Full-Text Encyclopedia/Digital Library.
● 261
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