Format: 16
Pricing: 32.00 yuan Network Information Retrieval introduces the principles and technologies of network information retrieval in detail, including information retrieval model, automatic acquisition of network information, network information preprocessing and indexing, query language and query optimization. In view of the wide application of network information retrieval, the key technologies such as search engine, Chinese and cross-language information retrieval, multimedia retrieval, parallel and distributed information retrieval, information classification and clustering, information extraction and automatic question answering are also discussed in depth.
"Network information retrieval" has distinct layers and simple explanation; There are not only principle elaboration and theoretical derivation, but also a large number of case studies, which strive to be systematic and scientific. Network Information Retrieval can be used as a textbook and reference book for senior undergraduates or postgraduates majoring in computer science and technology, information management and information system, and e-commerce in colleges and universities, and also has important reference value for scientific and technical personnel engaged in research and application development of network information retrieval, digital library, information management, artificial intelligence and Web data mining. Introduction to Chapter 1
1. 1 Overview of Network Information Retrieval
1. 1. 1 network information
1. 1.2 information retrieval
1. 1.3 network information retrieval
1.2 development of information retrieval
1.2. 1 manual search
1.2.2 offline batch retrieval
1.2.3 online search
1.2.4 network information retrieval
1.3 application of network information retrieval
1.3. 1 search engine
1.3.2 multimedia information retrieval
1.3.3 topic identification and tracking
1.3.4 information filtering
1.3.5 answers to questions
Think about a problem
refer to
Chapter II Information Retrieval Model
2. 1 Retrieve model definition
2.2 Boolean model
2.3 Vector model
2.3. 1 index project weight
similarity measurement
Calculation method
2,4 probability model
2.5 Extended Boolean Model
2.5. 1 fuzzy set model
Extended Boolean model
2.6 Extended Vector Model
2.6. 1 generalized vector space model
2.6.2 Latent Semantic Indexing Model
2.6.3 Neural network model
2.7 Extended Probability Model
2.7. 1 inference network model
2.7.2 Trust network model
2.7.3 Language model
2.8 Summary
Think about a problem
utilize
refer to
Chapter III Automatic Collection of Network Information
3. 1 Characteristics of network information
3. 1. 1 network composition
3. Features of1.2 Web
3.2 principles of network information collection
3.2. 1 Basic process of information collection
Ergodic strategy
Page parsing.
3.3 Politeness principle of network information collection
3.3. 1 robot exclusion protocol
Robot meta-tag
3.4 High-performance information collection
3.4. 1 Parallel Set
DNS optimization
3.4.3 Priority collection strategy
3.4.4 Web page update
3.4.5 Web page duplication removal
Avoid spider traps
3.5 Thematic information collection
3.5. 1 Page Theme Features
3.5.2 Thematic information collection algorithm
3.6 Summary
Think about a problem
utilize
refer to
Chapter 4 Network Text Processing and Indexing
4. 1 Features of the text
4. 1. 1 information entropy
4. 1.2 statistical method
4.2 Characteristics of network information
4.2. 1 Web page structure
4.2.2 Web page types
4.3 Web page denoising
4.3. 1 Method based on web page structure
Method based on template
4.4 Text processing
4.4. 1 lexical analysis
Exclude stop words
Stem extraction
4.4.4 Selection of Index Words
4.5 Index
4.5. 1 tree
Suffix tree
Signature file
Inverted file
4.6 Summary
Think about a problem
utilize
refer to
Chapter 5 Query Language and Query Processing
5. 1 Web query language
5. 1. 1 WebSQL query language
5. 1.2 W3QL query language
5. 1.3 WebOQL query language
5.2 Query method
5.2. 1 keyword query
pattern matching
5.3 Relevant feedback
5.3. 1 Correlation feedback in vector space model
5.3.2 Correlation feedback in probability model
5.4 Query expansion
5.4. 1 Simple query extension based on dictionary
Automatic local analysis
5,4.3 Automatic Global Analysis
5.5 Summary
Think about a problem
utilize
refer to
Chapter VI Performance Evaluation of Information Retrieval
6. 1 information retrieval evaluation index
6. 1. 1 recall rate and accuracy
6. 1.2 Other evaluation indicators
6.2 Information retrieval evaluation benchmark
6.2. 1 benchmark test
TREC assessment
6.2.3 Evaluation of Network Retrieval
6.2.4 CWIRF evaluation
6.3 Summary
Think about a problem
utilize
refer to
Chapter VII Search Engine
7. 1 overview
7. Development of1.1
7. 1.2 Terms and definitions
7. 1.3 working principle
7.2 Link analysis
7.2. 1 Page ranking
number of clicks
Comparison of algorithms
7.3 Relevant sorting
7.3. 1 Lucene retrieval model
7.3.2 Nutch sorting algorithm
7.4 Large-scale search engine
7.4. 1 architecture
data structure
7.4.3 retrieval algorithm
Correlation ranking
7.5 summary
Think about a problem
utilize
refer to
Chapter VIII Parallel and Distributed Information Retrieval
8. 1 parallel information retrieval
8. 1. 1 the concept of parallel computing
8. 1.2 parallel information retrieval architecture
8. 1.3 parallel programming
8. 1.4 data parallelism
8.2 Distributed Information Retrieval
8.3 yuan search engine
8.3. 1 system architecture
8.3.2 Resource selection
File selection
8.3.4 Information Fusion
8.4 P2P network information retrieval
8. 4. 1 P2P network information retrieval principle
8.4.2 Information Retrieval of Unstructured P2P Network
8.4.3 structured P2P network information retrieval
8.5 Summary
Think about a problem
utilize
refer to
Chapter 9 Chinese and Cross-language Information Retrieval
9. 1 Chinese preprocessing
9. 1. 1 Chinese coding and conversion
9. 1.2 Chinese word segmentation
9.2 Chinese information retrieval
9.2. 1 Chinese retrieval model
9.2.2 Chinese index
9.3 Cross-language information retrieval
9.3. 1 Basic principles
9.3.2 Cross-language Retrieval Based on GVSM
9.3.3 Cross-language Retrieval Based on LSI
9, 4 Summary
Think about a problem
utilize
refer to
10 Chapter Multimedia Information Retrieval
10. 1 Content-based image information retrieval
10.2 image feature extraction
1 color characteristics
10.2.2 shape feature extraction
10.2.3 texture feature extraction
10.3 image similarity measure
10.4 content-based video information retrieval
10.4. 1 shot segmentation
10.4.2 key frame extraction
10.5 content-based audio information retrieval
10.6 summary
Think about a problem
utilize
refer to
Chapter 1 1 Information Classification and Clustering
Basic knowledge 1 1. 1
The concept of class11.1.1
1 1. 1.2 object feature description
1 1. 1.3 document similarity
1 1. 1.4 interstage distance
1 1.2 feature description and extraction
1 1.2. 1 feature extraction
1 1.2.2 function selection
1 1.3 clustering method
1 1.3. 1 partition clustering method
1 1.3.2 hierarchical clustering method
1 1.3.3 Other clustering methods
1 1.4 classification method
1 1.4. 1 naive Bayesian algorithm
1 1.4.2 kNN algorithm
1 1.4.3 Rocchio algorithm
1 1.4.4 SVM algorithm
1 1.5 method evaluation
1 1.5. 1 evaluation of clustering method
1 1.5.2 evaluation of classification method
1 1.5.3 significance test
1 1.6 summary
Think about a problem
utilize
refer to
Chapter 12 Network Information Extraction and Question Answering System
12. 1 Overview of information extraction
12. 1. 1 development of information extraction
12. 1.2 information extraction evaluation index
12.2 Web information extraction
Web page information extraction based on keywords
12.2.2 Web information extraction based on schema
12.2.3 Web information extraction based on samples
12.3 question answering system
12.3. 1 problem analysis
12.3.2 information retrieval
12.3.3 answer extraction
12.6 summary
Think about a problem
refer to