《信息检索：算法与启发式方法(英文版·第2版)》是“信息检索”课程的优秀教材，书中对信息检索的概念、原理和算法进行了详细介绍，内容主要包括检索策略、检索实用工具、跨语言信息检索、查询处理、集成结构化及数据和文本、并行信息检索以及分布式信息检索等，并给出了阐述算法的大量实例。
《信息检索：算法与启发式方法(英文版·第2版)》有一定的深度和广度，而且所有的内容都用当前的技术阐述，是高等院校计算机及信息管理等相关专业本科生和研究生的理想教材，对信息检索领域的科研和技术人员也是很好的参考书。
格罗斯曼（David A.Grossman），佐治亚梅森大学博士。现在伊利诺伊理工大学计算机系任教。曾在美国政府部门高级技术服务中心和研究发展办公室担任项目经理。主要研究领域包括信息检索、结构化与非结构化数据集成以及数据挖掘。
弗里德（Ophir Frieder），伊利诺伊理工大学计算机系首席教授、学院数据检索实验室主任ACM会员，IEEE和美国艺术与科学研究院高级会员，他在数据检索系统、通信系统、高性能系统结构等方面均有研究。
1. INTRODUCTION
2. RETRIEVAL STRATEGIES
2.1 Vector Space Model
2.2 Probabilistic Retrieval Strategies
2.3 Language Models
2.4 Inference Networks
2.5 Extended Boolean Retrieval
2.6 Latent Semantic Indexing
2.7 Neural Networks
2.8 Genetic Algorithms
2.9 Fuzzy Set Retrieval
2.10 Summary
2.11 Exercises
3. RETRIEVAL UTILITIES
3.1 Relevance Feedback
3.2 Clustering
3.3 Passage-based Retrieval
3.4 N-grams
3.5 Regression Analysis
3.6 Thesauri
3.7 Semantic Networks
3.8 Parsing
3.9 Summary
3.10 Exercises
4. CROSS-LANGUAGE INFORMATION RETRIEVAL
4.1 Introduction
4.2 Crossing the Language Barrier
4.3 Cross-Language Retrieval Strategies
4.4 Cross Language Utilities
4.5 Summary
4.6 Exercises
5. EFFICIENCY
5.1 Inverted Index
5.2 Query Processing
5.3 Signature Files
5.4 Duplicate Document Detection
5.5 Summary
5.6 Exercises
6. INTEGRATING STRUCTURED DATA AND TEXT
6.1 Review of the Relational Model
6.2 A Historical Progression
6.3 Information Retrieval as a Relational Application
6.4 Semi-Structured Search using a Relational Schema
6.5 Multi-dimensional Data Model
6.6 Mediators
6.7 Summary
6.8 Exercises
7. PARALLEL INFORMATION RETRIEVAL
7.1 Parallel Text Scanning
7.2 Parallel Indexing
7.3 Clustering and Classification
7.4 Large Parallel Systems
7.5 Summary
7.6 Exercises
8. DISTRIBUTED INFORMATION RETRIEVAL
8.1 A Theoretical Model of Distributed Retrieval
8.2 Web Search
8.3 Result Fusion
8.4 Peer-to-Peer Information Systems
8.5 Other Architectures
8.6 Summary
8.7 Exercises
9. SUMMARY AND FUTURE DIRECTIONS
References
Index
3.4.1 DAmore and Mah
Initial information retrieval research focused on n-grams as presented in[DAmore and Mah, 1985]. The motivation behind their work was the fact thatit is difficult to develop mathematical models for terms since the potential fora term that has not been seen before is infinite. With n-grams, only a fixednumber of n-grams can exist for a given value of n. A mathematical modelwas developed to estimate the noise in indexing and to determine appropriatedocument similarity measures. DAmore and Mahs method replaces terms with n-grams in the vector spacemodel. The only remaining issue is computing the weights for each n-gram.Instead of simply using n-gram frequencies, a scaling method is used to nor-malize the length of the document. DAmore and Mahs contention was that alarge document contains more n-grams than a small document, so it should bescaled based on its length. To compute the weights for a given n-gram, DAmore and Mah estimatedthe number of occurrences of an n-gram in a document. The first simplifyingassumption was that n-grams occur with equal likelihood and follow a binomialdistribution. Hence, it was no more likely for n-gram "ABC" to occur than"DEE" The Zipfian distribution that is widely accepted for terms is not true forn-grams. DAmore and Mah noted that n-grams are not equally likely to occur,but the removal of frequently occurring terms from the document collectionresulted in n-grams that follow a more binomial distribution than the terms. DAmore and Mah computed the expected number of occurrences of an n-gram in a particular document. This is the product of the number of n-gramsin the document （the document length） and the probability that the n-gramoccurs. The n-grams probability of occurrence is computed as the ratio ofits number of occurrences to the total number of n-grams in the document.DAmore and Mah continued their application of the binomial distribution toderive an expected variance and, subsequently。
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