Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWWThe World Wide Web is rapidly filling with more text than anyone could have imagined a short time ago. However, the task of determining which data is relevant has become appreciably harder. In this original new work Richard Belew brings a cognitive science perspective to the study of information as a computer science discipline. He introduces the idea of Finding Out About (FOA), the process of actively seeking out information relevant to a topic of interest. Belew describes all facets of FOA, ranging from creating a good characterization of what the user seeks to evaluating the successful performance of search engines. His volume clearly shows how to build many of the tools that are useful for searching collections of text and other media. While computer scientists make up the book's primary audience, Belew skillfully presents technical details in a manner that makes important themes accessible to readers more comfortable with words than equations. Resources are available from the book's web site |
From inside the book
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... Tasks for Learning 255 7.1.3 Sources of Feedback 256 7.2 Building Hypotheses about Documents 258 7.2.1 Feature Selection 259 7.2.2 Hypothesis Spaces 262 7.3 Learning Which Documents to Route 263 7.3.1 Widrow - Hoff 7.3.2 User Drift and ...
... Tasks for Learning 255 7.1.3 Sources of Feedback 256 7.2 Building Hypotheses about Documents 258 7.2.1 Feature Selection 259 7.2.2 Hypothesis Spaces 262 7.3 Learning Which Documents to Route 263 7.3.1 Widrow - Hoff 7.3.2 User Drift and ...
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Contents
IV | 1 |
V | 8 |
VI | 10 |
VIII | 11 |
IX | 12 |
X | 13 |
XI | 14 |
XII | 16 |
XCI | 159 |
XCIII | 161 |
XCIV | 163 |
XCV | 165 |
XCVII | 166 |
XCVIII | 167 |
XCIX | 168 |
C | 169 |
XIII | 19 |
XIV | 20 |
XVI | 21 |
XVII | 22 |
XVIII | 26 |
XIX | 27 |
XX | 28 |
XXI | 29 |
XXII | 34 |
XXIII | 36 |
XXIV | 39 |
XXV | 40 |
XXVI | 42 |
XXVII | 44 |
XXVIII | 47 |
XXIX | 48 |
XXXI | 50 |
XXXII | 51 |
XXXIII | 54 |
XXXIV | 58 |
XXXV | 60 |
XXXVI | 62 |
XXXVII | 63 |
XXXVIII | 64 |
XXXIX | 66 |
XL | 67 |
XLI | 68 |
XLII | 71 |
XLIV | 73 |
XLV | 76 |
XLVI | 78 |
XLVII | 81 |
XLVIII | 83 |
XLIX | 84 |
L | 86 |
LI | 88 |
LII | 89 |
LIII | 92 |
LIV | 93 |
LV | 94 |
LVI | 95 |
LVII | 96 |
LVIII | 97 |
LIX | 101 |
LX | 105 |
LXI | 106 |
LXII | 109 |
LXIII | 111 |
LXIV | 115 |
LXV | 116 |
LXVIII | 118 |
LXIX | 119 |
LXX | 122 |
LXXI | 124 |
LXXII | 128 |
LXXIII | 129 |
LXXIV | 132 |
LXXV | 135 |
LXXVI | 137 |
LXXVII | 141 |
LXXIX | 142 |
LXXX | 143 |
LXXXI | 145 |
LXXXII | 146 |
LXXXIII | 149 |
LXXXIV | 152 |
LXXXV | 153 |
LXXXVII | 155 |
LXXXVIII | 156 |
LXXXIX | 157 |
XC | 158 |
CI | 170 |
CIII | 173 |
CIV | 175 |
CVI | 182 |
CVII | 185 |
CVIII | 187 |
CIX | 190 |
CX | 191 |
CXI | 193 |
CXII | 195 |
199 | |
CXIV | 200 |
CXV | 201 |
CXVI | 205 |
207 | |
CXVIII | 209 |
CXIX | 210 |
211 | |
213 | |
CXXII | 217 |
CXXIII | 220 |
CXXIV | 221 |
CXXV | 223 |
CXXVI | 224 |
225 | |
CXXIX | 234 |
CXXX | 238 |
CXXXII | 242 |
245 | |
CXXXIV | 247 |
CXXXV | 252 |
CXXXVII | 254 |
CXXXVIII | 255 |
CXXXIX | 256 |
CXL | 258 |
CXLI | 259 |
CXLII | 262 |
CXLIII | 263 |
CXLIV | 265 |
CXLV | 266 |
CXLVI | 267 |
CXLVII | 269 |
CXLVIII | 271 |
CXLIX | 272 |
CL | 273 |
CLI | 274 |
CLII | 275 |
CLIII | 277 |
CLIV | 279 |
CLV | 281 |
CLVI | 284 |
CLVII | 286 |
CLIX | 288 |
CLXI | 292 |
CLXIV | 294 |
CLXV | 298 |
CLXVI | 299 |
CLXVII | 304 |
CLXVIII | 305 |
CLXIX | 306 |
CLXX | 307 |
CLXXI | 308 |
CLXXII | 309 |
CLXXIII | 311 |
CLXXIV | 313 |
CLXXV | 316 |
CLXXVI | 318 |
CLXXVII | 321 |
347 | |
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Finding Out About: A Cognitive Perspective on Search Engine Technology and ... Richard K. Belew No preview available - 2008 |
Common terms and phrases
activity algorithm analysis ARTIFICIAL INTELLIGENCE associated assume assumption authors basic Bayesian network Belew capture Chapter citation classification cluster cognitive communication computer science consider contains context corpora corpus corresponding database defined dimensions discussed distribution docu domain of discourse evaluation example frequency frequency statistics function hitlist hypertext imagine important information need Information Retrieval interdocument interested keywords language lexical lexical analyzer linguistic machine learning match means measure methods NDoc nodes noise words NRel number of documents occur particular performance precision probabilistic probability problem query and document question rank Rank(d recall relation relevance assessments relevance feedback relevant documents RelFbk representation Retr retrieved documents Rijsbergen Salton SCSI search engine Section semantic shown in Figure similar simple statistics structure techniques textual tion tokens topic typically vector space vector space model vocabulary weight WordNet Zipf's law Zipfian Distribution