图书介绍
文本挖掘 英文PDF|Epub|txt|kindle电子书版本网盘下载
- （以）RonenFeldman，（美）JamesSanger著 著
- 出版社： 北京：人民邮电出版社
- ISBN：9787115205353
- 出版时间：2009
- 标注页数：410页
- 文件大小：65MB
- 文件页数：421页
- 主题词：数据采集－研究－英文
PDF下载
下载说明
文本挖掘 英文PDF格式电子书版下载
下载的文件为RAR压缩包。需要使用解压软件进行解压得到PDF格式图书。建议使用BT下载工具Free Download Manager进行下载,简称FDM(免费,没有广告,支持多平台）。本站资源全部打包为BT种子。所以需要使用专业的BT下载软件进行下载。如BitComet qBittorrent uTorrent等BT下载工具。迅雷目前由于本站不是热门资源。不推荐使用！后期资源热门了。安装了迅雷也可以迅雷进行下载！
（文件页数 要大于 标注页数，上中下等多册电子书除外）
注意：本站所有压缩包均有解压码： 点击下载压缩包解压工具
图书目录
Ⅰ．Introduction to Text Mining1
Ⅰ.1 Defining Text Mining1
Ⅰ.2 General Architecture of Text Mining Systems13
Ⅱ．Core Text Mining Operations19
Ⅱ.1 Core Text Mining Operations19
Ⅱ.2 Using Background Knowledge for Text Mining41
Ⅱ.3 Text Mining Query Languages51
Ⅲ．Text Mining Preprocessing Techniques57
Ⅲ.1 Task-Oriented Approaches58
Ⅲ.2 Further Reading62
Ⅳ．Categorization64
Ⅳ.1 Applications of Text Categorization65
Ⅳ.2 Definition of the Problem66
Ⅳ.3 Document Representation68
Ⅳ.4 Knowledge Engineering Approach to TC70
Ⅳ.5 Machine Learning Approach to TC70
Ⅳ.6 Using Unlabeled Data to Improve Classification78
Ⅳ.7 Evaluation of Text Classifiers79
Ⅳ.8 Citations and Notes80
Ⅴ．Clustering82
Ⅴ.1 Clustering Tasks in Text Analysis82
Ⅴ.2 The General Clustering Problem84
Ⅴ.3 Clustering Algorithms85
Ⅴ.4 Clustering of Textual Data88
Ⅴ.5 Citations and Notes92
Ⅵ．Information Extraction94
Ⅵ.1 Introduction to Information Extraction94
Ⅵ.2 Historical Evolution of IE:The Message Understanding Conferences and Tipster96
Ⅵ.3 IE Examples101
Ⅵ.4 Architecture of IE Systems104
Ⅵ.5 Anaphora Resolution109
Ⅵ.6 Inductive Algorithms for IE119
Ⅵ.7 Structural IE122
Ⅵ.8 Further Reading129
Ⅶ．Probabilistic Models for Information Extraction131
Ⅶ.1 Hidden Markov Models131
Ⅶ.2 Stochastic Context-Free Grammars137
Ⅶ.3 Maximal Entropy Modeling138
Ⅶ.4 Maximal Entropy Markov Models140
Ⅶ.5 Conditional Random Fields142
Ⅶ.6 Further Reading145
Ⅷ．Preprocessing Applications Using Probabilistic and Hybrid Approaches146
Ⅷ.1 Applications of HMM to Textual Analysis146
Ⅷ.2 Using MEMM for Information Extraction152
Ⅷ.3 Applications of CRFs to Textual Analysis153
Ⅷ.4 TEG:Using SCFG Rules for Hybrid Statistical-Knowledge-Based IE155
Ⅷ.5 Bootstrapping166
Ⅷ.6 Further Reading175
Ⅸ．Presentation-Layer Considerations for Browsing and Query Refinement177
Ⅸ.1 Browsing177
Ⅸ.2 Accessing Constraints and Simple Specification Filters at the Presentation Layer185
Ⅸ.3 Accessing the Underlying Query Language186
Ⅸ.4 Citations and Notes187
Ⅹ．Visualization Approaches189
Ⅹ.1 Introduction189
Ⅹ.2 Architectural Considerations192
Ⅹ.3 Common Visualization Approaches for Text Mining194
Ⅹ.4 Visualization Techniques in Link Analysis225
Ⅹ.5 Real-World Example:The Document Explorer System235
Ⅺ．Link Analysis242
Ⅺ.1 Preliminaries242
Ⅺ.2 Automatic Layout of Networks244
Ⅺ.3 Paths and Cycles in Graphs248
Ⅺ.4 Centrality249
Ⅺ.5 Partitioning of Networks257
Ⅺ.6 Pattern Matching in Networks270
Ⅺ.7 Software Packages for Link Analysis271
Ⅺ.8 Citations and Notes272
Ⅻ．Text Mining Applications273
Ⅻ.1 General Considerations274
Ⅻ.2 Corporate Finance:Mining Industry Literature for Business Intelligence279
Ⅻ.3 A "Horizontal" Text Mining Application:Patent Analysis Solution Leveraging a Commercial Text Analytics Platform295
Ⅻ.4 Life Sciences Research:Mining Biological Pathway Information with Gene Ways307
Appendix A:DIAL:A Dedicated Information Extraction Language for Text Mining315
A.1 What Is the DIAL Language?315
A.2 Information Extraction in the DIAL Environment316
A.3 Text Tokenization318
A.4 Concept and Rule Structure318
A.5 Pattern Matching320
A.6 Pattern Elements321
A.7 Rule Constraints325
A.8 Concept Guards326
A.9 Complete DIAL Examples327
Bibliography335
Index389