Text mining application programming:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston, Mass.
Charles River Media
2008
|
| Ausgabe: | [Nachdr.] |
| Schriftenreihe: | Charles River Media programming series
|
| Schlagworte: | |
| Online-Zugang: | Table of contents Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xix, 412 S. Ill., graph. Darst. 1 CD-ROM (12 cm) |
| Format: | System requirements: Hardware. Pentium III processor; 64 MB RAM; 50 MB hard disk space. Software for Microsoft Windows or Linux. Perl 5.6 or greater (Active-State or Cygwin implementations), Apache, and MySQL. |
| ISBN: | 1584504609 9781584504603 |
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| 100 | 1 | |a Konchady, Manu |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Text mining application programming |c Manu Konchady |
| 250 | |a [Nachdr.] | ||
| 264 | 1 | |a Boston, Mass. |b Charles River Media |c 2008 | |
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Datensatz im Suchindex
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|---|---|
| adam_text | TEXT MINING APPLICATION PROGRAMMING MANU KONCHADY CHARLES RIVER MEDIA
BOSTON, MASSACHUSETTS * CONTENTS PREFACE XV ACKNOWLEDGMENTS XIX
INTRODUCTION 1 ORIGINSOF TEXT MINING 4 INFORMATION RETRIEVAL 4 NATURAL
LANGUAGE PROCESSING 5 UNDERSTANDING TEXT 7 POLYSEMY 8 SYNONYMY 9
APPLICATIONS 11 BUSINESS 11 MEDIANE AND LAW 16 SOCIETY 18 INFORMATION
VISUALIZATION 20 AN ARCHITECTURE FOR TEXT MINING APPLICATIONS 21 TEXT
MINING FUNCTIONS 23 A LAYERED MODEL 25 TEXT MINE INSTALLATION 27
SOFTWARE 27 USAGE 28 SUMMARY 29 REFERENCES 30 VII VUEI CONTENTS -
MATHEMATICS BACKGROUND 31 PROBABILITY 34 LEAST SQUARES METHOD 36 ENTROPY
37 RELATED-EVENT PROBABILITIES 38 BAYES S RULE 40 PROBABILITY
DISTRIBUTIONS 42 BINOMIAL DISTRIBUTION 42 POISSON DISTRIBUTION 45 NORMAL
DISTRIBUTION 47 SAMPLING DISTRIBUTIONS 48 T-DISTRIBUTION 50 ESTIMATION
51 EXPECTATION MAXIMIZATION ALGORITHM 52 HYPOTHESIS TESTING 55
CHI-SQUARE TEST 56 MATRICES 58 SINGULAR VALUE DECOMPOSITION 60 SUMMARY
62 REFERENCES 62 EXPLORING TEXT 63 WORDS 65 TOKEN ASSEMBLY 67 WORD
STERNS 72 BASE WORDS 73 WORD AND MEANING RELATIONSHIPS 74 PATTERNS IN
WORDS AND LETTERS 76 WORD STATISTICS 80 ZIPF S LAW 84 CONTENTS IX
SENTENCES 88 INDEXING DOCUMENT TEXT 91 FREQUENCY-BASED 93 STOPWORDS 96
INVERSE DOCUMENT FREQUENCY 97 LATENT SEMANTIC INDEXING 100 SUMMARY 110
REFERENCES 111 4 MARKOV MODELS AND POS TAGGING 113 HIDDEN MARKOV MODELS
118 OBSERVATION PROBABILITY 119 STATE SEQUENCE 121 PARAMETER ESTIMATION
123 POS TAGGERS 126 HMM TAGGERS 128 RULE-BASED TAGGERS 131 TRAINING A
TAGGER 137 BUILDING A TAGGER 140 WORD SENSE DISAMBIGUATION 144 AN
IMPLEMENTATION OF A WSD 145 EVALUATION OFWSDS 148 SUMMARY 149 REFERENCES
150 5 INFORMATION EXTRACTION 151 IE APPLICATIONS 152 ENTITY EXTRACTION
156 HMMS FOR ENTITY EXTRACTION 158 IMPLEMENTATION OF AN ENTITY EXTRACTOR
162 X CONTENTS IE SYSTEMS 170 FASTUS 172 RAPIER 175 PHRASE EXTRACTION
178 SUMMARY 181 REFERENCES 182 SEARCH ENGINES 183 EARLY SEARCH ENGINES
184 MEDLINE 185 DIALOG 186 INDEXING TEXT FOR SEARCH 187 AN
IMPLEMENTATION IN TEXT MINE 189 GOOGLE INDEX 192 INDEXING MULTIMEDIA 194
QUERIES 197 BOOLEAN QUERIES 198 MULTIMEDIA QUERIES 198 RELEVANCE
FEEDBACK 200 SEARCHING AN INDEX 202 SEARCHING IN TEXT MINE 203 GOOGLE
SEARCH 204 EVALUATION 205 RANKING ALGORITHMS 209 LINK STRUCTURE OF WEB
PAGES 210 VIEWING SEARCH RESULTS 218 SUMMARY 221 REFERENCES 222 CONTENTS
XI 7 SEARCHING THE WEB 225 WEB STRUCTURE 226 SEARCH ENGINE COVERAGE 229
WEB DIRECTORIES 233 A DISTRIBUTED SEARCH 234 WEB COMMUNITIES 236 THE
HIDDEN WEB 238 CRAWLERS 241 WEB SEARCH ENGINE CRAWLERS 242 FOCUSED
CRAWLERS 247 TEXT MINE CRAWLER 252 CRAWL VISUALIZATION 258 SUMMARY 260
REFERENCES 260 8 CLUSTERING DOCUMENTS 263 CLUSTER ORGANIZATION 265
CLUSTER PARAMETERS 267 CLUSTER-BASED SEARCH 267 SEARCHING WITH A
TAXONOMY 268 SIMILARITY MEASURES 269 LINKING METHODS 271 CLUSTERING
METHODS 272 K-MEANS 276 SIMULATED ANNEALING 279 GENETIC ALGORITHMS 282
SCATTER/GATHER 285 VISUAL TOOLS FOR CLUSTERS 287 CLUSTER EVALUATION 289
SUMMARY 297 REFERENCES 297 XII CONTENTS 9 TEXT CATEGORIZATION 299
CATEGORIZATION PROBLEM 300 FILTERING EMAIL 302 A BAYESIAN EMAIL FILTER
305 FEATURES OF SPAM 307 REQUIREMENTS FOR A SPAM DETECTOR 309 AN EMAIL
ARCHIVE 309 EMAIL CATEGORIZATION 311 EMAIL MONITOR 313 PERSONAL EMAIL
NETWORK 314 CHAIN EMAIL 315 CATEGORIZATION METHODS 317 ROCCHIO S
ALGORITHM 318 PERCEPTRONS 319 DECISION TREES 321 NEAREST NEIGHBOR 323
SUPPORT VECTOR MACHINES 325 SUMMARY 330 REFERENCES 331 10 SUMMARIZATION
333 TRAINING A SUMMARIZER 335 SENTENCE SELECTION 337 NEWS ARTICLES 338
EMAIL THREADS 339 WEB PAGES 342 A CLUSTER-BASED SUMMARIZER 345
IMPLEMENTATION OFA SUMMARIZER 349 EVALUATION OF SUMMARIES 352 CONTENTS
INFORMATION MONITOR 354 EVENT DETECTION 355 EVENT TRACKING 357
MONITORING THE NEWS 357 SENTIMENT ANALYSIS 362 SUMMARY 365 REFERENCES
365 11 QUESTION & ANSWER 367 QUESTION CLASSIFICATION 369 IMPLEMENTATION
OF A QUESTION CLASSIFIER 371 A NATURAL LANGUAGE FAQ 377 READING
COMPREHENSION 379 LOCAL Q&A 383 MURAX 383 TREC 385 WEB Q&A 386 ANSWERBUS
387 NSIR 389 COMPUTER-AIDED Q&A 394 FINDING EXPERTS 394 SUMMARY 395
REFERENCES 396 ABOUT THE CD-ROM 399 TEXT MINE 399 INSTALLATION 399
DATABASE MANAGEMENT 401 SECURITY 401 TESTING 401 CONTENTS * TOOLS 402
SOURCES 402 WORDNET 403 REUTERS 403 SVDPACKC 403 LISTS 403 INDEX 405
|
| adam_txt |
TEXT MINING APPLICATION PROGRAMMING MANU KONCHADY CHARLES RIVER MEDIA
BOSTON, MASSACHUSETTS * CONTENTS PREFACE XV ACKNOWLEDGMENTS XIX
INTRODUCTION 1 ORIGINSOF TEXT MINING 4 INFORMATION RETRIEVAL 4 NATURAL
LANGUAGE PROCESSING 5 UNDERSTANDING TEXT 7 POLYSEMY 8 SYNONYMY 9
APPLICATIONS 11 BUSINESS 11 MEDIANE AND LAW 16 SOCIETY 18 INFORMATION
VISUALIZATION 20 AN ARCHITECTURE FOR TEXT MINING APPLICATIONS 21 TEXT
MINING FUNCTIONS 23 A LAYERED MODEL 25 TEXT MINE INSTALLATION 27
SOFTWARE 27 USAGE 28 SUMMARY 29 REFERENCES 30 VII VUEI CONTENTS -
MATHEMATICS BACKGROUND 31 PROBABILITY 34 LEAST SQUARES METHOD 36 ENTROPY
37 RELATED-EVENT PROBABILITIES 38 BAYES'S RULE 40 PROBABILITY
DISTRIBUTIONS 42 BINOMIAL DISTRIBUTION 42 POISSON DISTRIBUTION 45 NORMAL
DISTRIBUTION 47 SAMPLING DISTRIBUTIONS 48 T-DISTRIBUTION 50 ESTIMATION
51 EXPECTATION MAXIMIZATION ALGORITHM 52 HYPOTHESIS TESTING 55
CHI-SQUARE TEST 56 MATRICES 58 SINGULAR VALUE DECOMPOSITION 60 SUMMARY
62 REFERENCES 62 EXPLORING TEXT 63 WORDS 65 TOKEN ASSEMBLY 67 WORD
STERNS 72 BASE WORDS 73 WORD AND MEANING RELATIONSHIPS 74 PATTERNS IN
WORDS AND LETTERS 76 WORD STATISTICS 80 ZIPF'S LAW 84 CONTENTS IX
SENTENCES 88 INDEXING DOCUMENT TEXT 91 FREQUENCY-BASED 93 STOPWORDS 96
INVERSE DOCUMENT FREQUENCY 97 LATENT SEMANTIC INDEXING 100 SUMMARY 110
REFERENCES 111 4 MARKOV MODELS AND POS TAGGING 113 HIDDEN MARKOV MODELS
118 OBSERVATION PROBABILITY 119 STATE SEQUENCE 121 PARAMETER ESTIMATION
123 POS TAGGERS 126 HMM TAGGERS 128 RULE-BASED TAGGERS 131 TRAINING A
TAGGER 137 BUILDING A TAGGER 140 WORD SENSE DISAMBIGUATION 144 AN
IMPLEMENTATION OF A WSD 145 EVALUATION OFWSDS 148 SUMMARY 149 REFERENCES
150 5 INFORMATION EXTRACTION 151 IE APPLICATIONS 152 ENTITY EXTRACTION
156 HMMS FOR ENTITY EXTRACTION 158 IMPLEMENTATION OF AN ENTITY EXTRACTOR
162 X CONTENTS IE SYSTEMS 170 FASTUS 172 RAPIER 175 PHRASE EXTRACTION
178 SUMMARY 181 REFERENCES 182 SEARCH ENGINES 183 EARLY SEARCH ENGINES
184 MEDLINE 185 DIALOG 186 INDEXING TEXT FOR SEARCH 187 AN
IMPLEMENTATION IN TEXT MINE 189 GOOGLE INDEX 192 INDEXING MULTIMEDIA 194
QUERIES 197 BOOLEAN QUERIES 198 MULTIMEDIA QUERIES 198 RELEVANCE
FEEDBACK 200 SEARCHING AN INDEX 202 SEARCHING IN TEXT MINE 203 GOOGLE
SEARCH 204 EVALUATION 205 RANKING ALGORITHMS 209 LINK STRUCTURE OF WEB
PAGES 210 VIEWING SEARCH RESULTS 218 SUMMARY 221 REFERENCES 222 CONTENTS
XI 7 SEARCHING THE WEB 225 WEB STRUCTURE 226 SEARCH ENGINE COVERAGE 229
WEB DIRECTORIES 233 A DISTRIBUTED SEARCH 234 WEB COMMUNITIES 236 THE
HIDDEN WEB 238 CRAWLERS 241 WEB SEARCH ENGINE CRAWLERS 242 FOCUSED
CRAWLERS 247 TEXT MINE CRAWLER 252 CRAWL VISUALIZATION 258 SUMMARY 260
REFERENCES 260 8 CLUSTERING DOCUMENTS 263 CLUSTER ORGANIZATION 265
CLUSTER PARAMETERS 267 CLUSTER-BASED SEARCH 267 SEARCHING WITH A
TAXONOMY 268 SIMILARITY MEASURES 269 LINKING METHODS 271 CLUSTERING
METHODS 272 K-MEANS 276 SIMULATED ANNEALING 279 GENETIC ALGORITHMS 282
SCATTER/GATHER 285 VISUAL TOOLS FOR CLUSTERS 287 CLUSTER EVALUATION 289
SUMMARY 297 REFERENCES 297 XII CONTENTS 9 TEXT CATEGORIZATION 299
CATEGORIZATION PROBLEM 300 FILTERING EMAIL 302 A BAYESIAN EMAIL FILTER
305 FEATURES OF SPAM 307 REQUIREMENTS FOR A SPAM DETECTOR 309 AN EMAIL
ARCHIVE 309 EMAIL CATEGORIZATION 311 EMAIL MONITOR 313 PERSONAL EMAIL
NETWORK 314 CHAIN EMAIL 315 CATEGORIZATION METHODS 317 ROCCHIO'S
ALGORITHM 318 PERCEPTRONS 319 DECISION TREES 321 NEAREST NEIGHBOR 323
SUPPORT VECTOR MACHINES 325 SUMMARY 330 REFERENCES 331 10 SUMMARIZATION
333 TRAINING A SUMMARIZER 335 SENTENCE SELECTION 337 NEWS ARTICLES 338
EMAIL THREADS 339 WEB PAGES 342 A CLUSTER-BASED SUMMARIZER 345
IMPLEMENTATION OFA SUMMARIZER 349 EVALUATION OF SUMMARIES 352 CONTENTS
INFORMATION MONITOR 354 EVENT DETECTION 355 EVENT TRACKING 357
MONITORING THE NEWS 357 SENTIMENT ANALYSIS 362 SUMMARY 365 REFERENCES
365 11 QUESTION & ANSWER 367 QUESTION CLASSIFICATION 369 IMPLEMENTATION
OF A QUESTION CLASSIFIER 371 A NATURAL LANGUAGE FAQ 377 READING
COMPREHENSION 379 LOCAL Q&A 383 MURAX 383 TREC 385 WEB Q&A 386 ANSWERBUS
387 NSIR 389 COMPUTER-AIDED Q&A 394 FINDING EXPERTS 394 SUMMARY 395
REFERENCES 396 ABOUT THE CD-ROM 399 TEXT MINE 399 INSTALLATION 399
DATABASE MANAGEMENT 401 SECURITY 401 TESTING 401 CONTENTS * TOOLS 402
SOURCES 402 WORDNET 403 REUTERS 403 SVDPACKC 403 LISTS 403 INDEX 405 |
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| index_date | 2024-07-02T22:25:59Z |
| indexdate | 2024-07-09T21:23:08Z |
| institution | BVB |
| isbn | 1584504609 9781584504603 |
| language | English |
| lccn | 2006002985 |
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| physical | xix, 412 S. Ill., graph. Darst. 1 CD-ROM (12 cm) |
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| spelling | Konchady, Manu Verfasser aut Text mining application programming Manu Konchady [Nachdr.] Boston, Mass. Charles River Media 2008 xix, 412 S. Ill., graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Charles River Media programming series Includes bibliographical references and index System requirements: Hardware. Pentium III processor; 64 MB RAM; 50 MB hard disk space. Software for Microsoft Windows or Linux. Perl 5.6 or greater (Active-State or Cygwin implementations), Apache, and MySQL. Data mining Text Mining (DE-588)4728093-1 gnd rswk-swf Text Mining (DE-588)4728093-1 s DE-604 http://www.loc.gov/catdir/toc/ecip067/2006002985.html Table of contents GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016803437&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Konchady, Manu Text mining application programming Data mining Text Mining (DE-588)4728093-1 gnd |
| subject_GND | (DE-588)4728093-1 |
| title | Text mining application programming |
| title_auth | Text mining application programming |
| title_exact_search | Text mining application programming |
| title_exact_search_txtP | Text mining application programming |
| title_full | Text mining application programming Manu Konchady |
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| title_full_unstemmed | Text mining application programming Manu Konchady |
| title_short | Text mining application programming |
| title_sort | text mining application programming |
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