文本挖掘
￥ 25 3.6折 ￥ 69 九五品
仅1件
作者(以)费尔德曼,(美)桑格 著
出版社人民邮电出版社
ISBN9787115205353
出版时间2009-08
版次1
装帧平装
开本16开
纸张胶版纸
页数410页
字数99999千字
定价69元
上书时间2024-03-24
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基本信息
书名:文本挖掘
定价：69.00元
作者:(以)费尔德曼,(美)桑格 著
出版社：人民邮电出版社
出版日期：2009-08-01
ISBN：9787115205353
字数：506000
页码：410
版次：1
装帧：平装
开本：16开
商品重量：
编辑推荐
“……我购买了这本书。这本书是非常值得拥有的参考书。” ——L. Venkata Subramaniam，IBM印度研究实验室 “一本由该领域重要专家编写的文本挖掘导论。这本书写得非常好，地结合了文本挖掘的理论和实践，既适合研究人员又适合实践者……极力推荐那些没有任何计算语言学背景而想钻研文本挖掘领域的人阅读本书。” ——Rada Mihalcea，北得克萨斯大学
内容提要
本书是一部文本挖掘领域名著，作者为世界知名的学者。书中涵盖了核心文本挖掘操作、文本挖掘预处理技术、分类、聚类、信息提取、信息提取的概率模型、预处理应用、可视化方法、链接分析、文本挖掘应用等内容，很好地结合了文本挖掘的理论和实践。 本书非常适合文本挖掘、信息检索领域的研究人员和实践者阅读，也适合作为高等院校计算机及相关专业研究生的数据挖掘和知识发现等课程的教材。
目录
Ⅰ. Introduction to Text Mining Ⅰ.1 Defining Text Mining Ⅰ.2 General Architecture of Text Mining SystemsⅡ. Core Text Mining Operations Ⅱ.1 Core Text Mining Operations Ⅱ.2 Using Background Knowledge for Text Mining Ⅱ.3 Text Mining Query LanguagesⅢ. Text Mining Preprocessing Techniques Ⅲ.1 Task-Oriented Approaches Ⅲ.2 Further ReadingⅣ. Categorization Ⅳ.1 Applications of Text Categorization Ⅳ.2 Definition of the Problem Ⅳ.3 Document Representation Ⅳ.4 Knowledge Engineering Approach to TC Ⅳ.5 Machine Learning Approach to TC Ⅳ.6 Using Unlabeled Data to Improve Classification Ⅳ.7 Evaluation of Text Classifiers Ⅳ.8 Citations and NotesⅤ. Clustering Ⅴ.1 Clustering Tasks in Text Analysis Ⅴ.2 The General Clustering Problem Ⅴ.3 Clustering Algorithms Ⅴ.4 Clustering of Textual Data Ⅴ.5 Citations and NotesⅥ. Information Extraction Ⅵ.1 Introduction to Information Extraction Ⅵ.2 Historical Evolution of IE: The Message Understanding Conferences and Tipster Ⅵ.3 IE Examples Ⅵ.4 Architecture of IE Systems Ⅵ.5 Anaphora Resolution Ⅵ.6 Inductive Algorithms for IE Ⅵ.7 Structural IE Ⅵ.8 Further ReadingⅦ. Probabilistic Models for Information Extraction Ⅶ.1 Hidden Markov Models Ⅶ.2 Stochastic Context-Free Grammars Ⅶ.3 Maximal Entropy Modeling Ⅶ.4 Maximal Entropy Markov Models Ⅶ.5 Conditional Random Fields Ⅶ.6 Further ReadingⅧ. Preprocessing Applications Using Probabilistic and Hybrid Approaches Ⅷ.1 Applications of HMM to Textual Analysis Ⅷ.2 Using MEMM for Information Extraction Ⅷ.3 Applications of CRFs to Textual Analysis Ⅷ.4 TEG: Using SCFG Rules for Hybrid Statistical–Knowledge-Based IE Ⅷ.5 Bootstrapping Ⅷ.6 Further ReadingⅨ. Presentation-Layer Considerations for Browsing and Query Refinement Ⅸ.1 Browsing Ⅸ.2 Accessing Constraints and Simple Specification Filters at the Presentation Layer Ⅸ.3 Accessing the Underlying Query Language Ⅸ.4 Citations and NotesⅩ. Visualization Approaches Ⅹ.1 Introduction Ⅹ.2 Architectural Considerations Ⅹ.3 Common Visualization Approaches for Text Mining Ⅹ.4 Visualization Techniques in Link Analysis Ⅹ.5 Real-World Example: The Document Explorer SystemⅪ. Link Analysis Ⅺ.1 Preliminaries Ⅺ.2 Automatic Layout of Networks Ⅺ.3 Paths and Cycles in Graphs Ⅺ.4 Centrality Ⅺ.5 Partitioning of Networks Ⅺ.6 Pattern Matching in Networks Ⅺ.7 Software Packages for Link Analysis Ⅺ.8 Citations and NotesⅫ. Text Mining Applications Ⅻ.1 General Considerations Ⅻ.2 Corporate Finance: Mining Industry Literature for Business Intelligence Ⅻ.3 A “Horizontal” Text Mining Application: Patent Analysis Solution Leveraging a Commercial Text Analytics Platform Ⅻ.4 Life Sciences Research: Mining Biological Pathway Information with GeneWaysAppendix A: DIAL: A Dedicated Information Extraction Language forText Mining A.1 What Is the DIAL Language? A.2 Information Extraction in the DIAL Environment A.3 Text Tokenization A.4 Concept and Rule Structure A.5 Pattern Matching A.6 Pattern Elements A.7 Rule Constraints A.8 Concept Guards A.9 Complete DIAL ExamplesBibliographyIndex
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