Building search applications: Lucene, LingPipe, and Gate
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oakton, VA
Mustru Publ.
2008
|
| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIV, 430 S. Ill., graph. Darst. 25 cm |
| ISBN: | 9780615204253 |
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| 250 | |a 1. ed. | ||
| 264 | 1 | |a Oakton, VA |b Mustru Publ. |c 2008 | |
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| 650 | 4 | |a Web search engines | |
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| 650 | 4 | |a Data mining | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Titel: Building search applications
Autor: Konchady, Manu
Jahr: 2008
Contents
Preface
IX
1 Information Overload 1
1.1 Information Sources ........................... 3
1.2 Information Management Tools..................... 5
1.2.1 Search Engines.......................... 6
1.2.2 Entity Extraction ........................ 7
1.2.3 Organizing Information ..................... 9
1.2.4 Tracking Information ...................... 9
1.3 Visualization ............................... 10
1.3.1 Social Network Visualization................... 11
1.3.2 Stock Price and News Visualization............... 11
1.3.3 Tag Clouds............................ 12
1.4 Applications ............................... 13
1.4.1 Spam Detection ......................... 14
1.4.2 Email Usage and Management.................. 15
1.4.3 Customer Service......................... 17
1.4.4 Employee Surveys ........................ 17
1.4.5 Other Applications........................ 18
2 Tokenizing Text 21
2.1 Character Sets .............................. 21
2.1.1 Tokens............................... 22
2.2 Lucene Analyzers............................. 24
2.2.1 WhitespaceAnalyzer....................... 24
2.2.2 SimpleAnalyzer.......................... 26
2.2.3 Analyzer Design.......................... 27
2.2.4 StandardAnalyzer......................... 28
2.2.5 PorterAnalyzer.......................... 29
2.2.6 StandardBgramAnalyzer..................... 32
2.2.7 Other Analyzers.......................... 35
2.3 LingPipe Tokenizers ........................... 36
2.3.1 IndoEuropeanTokenizer ..................... 36
2.3.2 Filtered Tokenizers........................ 37
2.3.3 Regular Expression Tokenizer.................. 38
2.3.4 Character-based Ngram Tokenizer................ 38
2.3.5 A LingPipe Tokenizer in a Lucene Analyzer.......... 39
2.3.6 A Lucene Analyzer in a LingPipe Tokenizer.......... 41
2.4 Gate Tokenizer.............................. 43
2.4.1 A Gate Tokenizer in a Lucene Analyzer............. 47
2.5 Tokenizing Problems........................... 49
2.6 Text Extraction.............................. 55
2.7 WordNet.................................. 61
2.7.1 Word Stems and WordNet.................... 67
2.8 Summary................................. 69
Indexing Text with Lucene 71
3.1 Databases and Search Engines...................... 71
3.2 Early Search Engines........................... 73
3.2.1 Web Search Engines and IR Systems.............. 74
3.3 Generating an Index........................... 75
3.3.1 Term Weighting.......................... 77
3.3.2 Term Vector Model........................ 82
3.3.3 Inverted Index........................... 82
3.4 Creating an Index with Lucene ..................... 84
3.4.1 Field Attributes.......................... 87
3.4.2 Boosting.............................. 89
3.5 Modifying an Index with Lucene..................... 90
3.6 A Database Backed Index........................ 93
3.6.1 Deleting a Document....................... 96
3.6.2 Updating a Document...................... 98
3.7 Maintaining an Index........................... 98
3.7.1 Logs................................ 101
3.7.2 Transactions............................ 102
3.7.3 Database Index Synchronization................. 102
3.7.4 Lucene Index Files........................ 103
3.8 Performance................................ 106
3.8.1 Index Tuning Parameters..................... 106
3.8.2 Evaluation of Parameters..................... 108
3.8.3 Memory-Based Index....................... 110
3.8.4 Index Performance with a Database............... 113
3.8.5 Index Scalability......................... 114
3.8.6 Index Vocabulary......................... 115
3.9 Date Fields................................ 117
3.10 Metadata................................. 120
3.10.1 Document Metadata....................... 120
3.10.2 Multimedia Metadata....................... 121
3.10.3 Metadata Standards....................... 123
3.11 Summary................................. 124
Searching Text with Lucene 125
4.1 Lucene Search Architecture ....................... 125
4.2 Search Interface Design.......................... 128
4.3 Search Behavior.............................. 130
4.3.1 Intranets and the Web...................... 132
4.4 Searching the Index............................ 134
4.4.1 Generating Queries with QueryParser.............. 137
4.4.2 Expanded Queries ........................ 142
4.4.3 Span Queries........................... 147
4.5 Query Performance............................ 148
4.6 Organizing Results............................ 150
4.6.1 Sorting Results.......................... 151
4.6.2 Scoring Results.......................... 152
4.6.3 Customizing Query-Doc Similarity ............... 158
4.7 Filtering Queries............................. 159
4.7.1 Range Filter............................ 159
4.7.2 Security Filter........................... 161
in
4.7.3 Query Filter............................ 163
4.7.4 Caching Filters.......................... 164
4.7.5 Chained Filters.......................... 165
4.8 Modifying Queries ............................ 167
4.8.1 Spell Check............................ 168
4.8.2 Finding Similar Documents ................... 177
4.9 Troubleshooting a Query......................... 179
4.10 Summary................................. 180
Tagging Text 183
5.1 Sentences ................................. 184
5.1.1 Sentence Extraction with LingPipe............... 184
5.1.2 Sentence Extraction with Gate ................. 187
5.1.3 Text Extraction from Web Pages................ 191
5.2 Part of Speech Taggers.......................... 192
5.2.1 Tag Sets.............................. 193
5.2.2 Markov Models.......................... 195
5.2.3 Evaluation of a Tagger...................... 199
5.2.4 POS Tagging with LingPipe................... 200
5.2.5 Rule-Based Tagging........................ 206
5.2.6 POS Tagging with Gate..................... 208
5.2.7 Markov Model vs Rule-based Taggers.............. 211
5.3 Phrase Extraction............................. 211
5.3.1 Applications............................ 212
5.3.2 Finding Phrases.......................... 213
5.3.3 Likelihood Ratio......................... 214
5.3.4 Phrase Extraction using LingPipe................ 218
5.3.5 Current Phrases.......................... 221
5.4 Entity Extraction............................. 223
5.4.1 Applications............................ 223
5.4.2 Entity Extraction with Gate................... 225
5.4.3 Entity Extraction with LingPipe ................ 232
5.4.4 Evaluation............................. 238
5.4.5 Entity Extraction Errors..................... 239
5.5 Summary................................. 239
IV
6 Organizing Text: Clustering 241
6.1 Applications................................ 241
6.2 Creating Clusters............................. 243
6.2.1 Clustering Documents...................... 245
6.2.2 Similarity Measures........................ 246
6.2.3 Comparison of Similarity Measures............... 251
6.2.4 Using the Similarity Matrix................... 254
6.3 Cluster Algorithms............................ 256
6.3.1 Global Optimization Methods.................. 257
6.3.2 Heuristic Methods ........................ 257
6.3.3 Agglomerative Methods ..................... 259
6.4 Building Clusters with LingPipe .................... 262
6.4.1 Debugging Clusters........................ 265
6.4.2 Evaluating Clusters........................ 266
6.5 Summary................................. 270
7 Organizing Text: Categorization 273
7.1 Categorization Problem ......................... 273
7.1.1 Applications for Document Categorization........... 274
7.2 Categorizing Documents......................... 278
7.2.1 Training the Model........................ 279
7.2.2 Using the Model . ,....................... 280
7.3 Categorization Methods ......................... 281
7.3.1 Character-based Ngram Models................. 282
7.3.2 Binary and Multi Classifiers................... 283
7.3.3 TF/IDF Classifier......................... 288
7.3.4 K-Nearest Neighbors Classifier.................. 290
7.3.5 Naive Bayes Classifier ...................... 292
7.3.6 Evaluation............................. 293
7.3.7 Feature Extraction........................ 294
7.4 Summary................................. 296
8 Searching an Intranet and the Web 297
8.1 Early Web Search Engines........................ 297
8.2 Web Structure............................... 298
8.2.1 A Bow-Tie Web Graph...................... 299
8.2.2 Hubs Authorities........................ 304
8.2.3 PageRank Algorithm....................... 307
8.2.4 PageRank vs. Hubs k Authorities................ 310
8.3 Crawlers ................................. 313
8.3.1 Building a Crawler........................ 314
8.3.2 Search Engine Coverage..................... 319
8.4 Nutch................................... 321
8.4.1 Nutch Crawler .......................... 322
8.4.2 Crawl Configuration....................... 325
8.4.3 Running a Re-crawl........................ 329
8.4.4 Search Interface.......................... 330
8.4.5 Troubleshooting.......................... 334
8.5 Summary................................. 336
9 Tracking Information 339
9.1 News Monitoring............................. 339
9.1.1 Web Feeds............................. 339
9.1.2 NewsRack............................. 341
9.2 Sentiment Analysis............................ 344
9.2.1 Automatic Classification..................... 345
9.2.2 An Implementation with LingPipe ............... 351
9.3 Detecting Offensive Content....................... 358
9.3.1 Detection Methods........................ 358
9.4 Plagiarism Detection........................... 360
9.4.1 Forms of Plagiarism ....................... 360
9.4.2 Methods to Detect Plagiarism.................. 361
9.4.3 Copy Detection using SCAM .................. 363
9.4.4 Other Applications........................ 366
9.5 Summary ................................. 367
10 Future Directions in Search 369
10.1 Improving Search Engines........................370
10.1.1 Adding Human Intelligence ...................370
10.1.2 Special Features..........................372
VI
10.1.3 OpenSearch............................ 374
10.1.4 Specialized Search Engines.................... 379
10.2 Using Collective Intelligence to Improve Search ............ 382
10.2.1 Tag-Based Search Engines.................... 383
10.3 Question k Answer............................ 387
10.3.1 Q A Engine Design....................... 388
10.3.2 Performance............................ 391
10.4 Summary ................................. 392
Appendix A Software 393
Appendix B Bayes Classification 403
Appendix C The Berkeley DB 407
Index 417
vn
|
| any_adam_object | 1 |
| author | Konchady, Manu |
| author_facet | Konchady, Manu |
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| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | ST 250 ST 252 |
| ctrlnum | (OCoLC)753862605 (DE-599)BVBBV036106931 |
| dewey-full | 005.276 |
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| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.276 |
| dewey-search | 005.276 |
| dewey-sort | 15.276 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 1. ed. |
| format | Book |
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| language | English |
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| publisher | Mustru Publ. |
| record_format | marc |
| spelling | Konchady, Manu Verfasser aut Building search applications Lucene, LingPipe, and Gate Manu Konchady 1. ed. Oakton, VA Mustru Publ. 2008 XIV, 430 S. Ill., graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Lucene (Electronic resource) LingPipe (Electronic resource) Exploration de données ram Logiciels libres ram Moteurs de recherche - Programmation ram Moteurs de recherche sur Internet ram Search engines Programming Web search engines Text processing (Computer science) Data mining Open source software Volltext (DE-588)4740819-4 gnd rswk-swf Lucene (DE-588)4800725-0 gnd rswk-swf Java Programmiersprache (DE-588)4401313-9 gnd rswk-swf Suchmaschine (DE-588)4423007-2 gnd rswk-swf Open Source (DE-588)4548264-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Suchmaschine (DE-588)4423007-2 s Lucene (DE-588)4800725-0 s Data Mining (DE-588)4428654-5 s Open Source (DE-588)4548264-0 s DE-604 Volltext (DE-588)4740819-4 s Java Programmiersprache (DE-588)4401313-9 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018997178&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Konchady, Manu Building search applications Lucene, LingPipe, and Gate Lucene (Electronic resource) LingPipe (Electronic resource) Exploration de données ram Logiciels libres ram Moteurs de recherche - Programmation ram Moteurs de recherche sur Internet ram Search engines Programming Web search engines Text processing (Computer science) Data mining Open source software Volltext (DE-588)4740819-4 gnd Lucene (DE-588)4800725-0 gnd Java Programmiersprache (DE-588)4401313-9 gnd Suchmaschine (DE-588)4423007-2 gnd Open Source (DE-588)4548264-0 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4740819-4 (DE-588)4800725-0 (DE-588)4401313-9 (DE-588)4423007-2 (DE-588)4548264-0 (DE-588)4428654-5 |
| title | Building search applications Lucene, LingPipe, and Gate |
| title_auth | Building search applications Lucene, LingPipe, and Gate |
| title_exact_search | Building search applications Lucene, LingPipe, and Gate |
| title_full | Building search applications Lucene, LingPipe, and Gate Manu Konchady |
| title_fullStr | Building search applications Lucene, LingPipe, and Gate Manu Konchady |
| title_full_unstemmed | Building search applications Lucene, LingPipe, and Gate Manu Konchady |
| title_short | Building search applications |
| title_sort | building search applications lucene lingpipe and gate |
| title_sub | Lucene, LingPipe, and Gate |
| topic | Lucene (Electronic resource) LingPipe (Electronic resource) Exploration de données ram Logiciels libres ram Moteurs de recherche - Programmation ram Moteurs de recherche sur Internet ram Search engines Programming Web search engines Text processing (Computer science) Data mining Open source software Volltext (DE-588)4740819-4 gnd Lucene (DE-588)4800725-0 gnd Java Programmiersprache (DE-588)4401313-9 gnd Suchmaschine (DE-588)4423007-2 gnd Open Source (DE-588)4548264-0 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Lucene (Electronic resource) LingPipe (Electronic resource) Exploration de données Logiciels libres Moteurs de recherche - Programmation Moteurs de recherche sur Internet Search engines Programming Web search engines Text processing (Computer science) Data mining Open source software Volltext Lucene Java Programmiersprache Suchmaschine Open Source Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018997178&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT konchadymanu buildingsearchapplicationslucenelingpipeandgate |