Information Extraction: Algorithms and Prospects in a Retrieval Context (eBook)
XIV, 246 Seiten
Springer Netherland (Verlag)
978-1-4020-4993-4 (ISBN)
This book covers content recognition in text, elaborating on past and current most successful algorithms and their application in a variety of settings: news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text. Today, there is considerable interest in integrating the results of information extraction in retrieval systems, because of the demand for search engines that return precise answers to flexible information queries.
Currently, there is a considerable interest in integrating the results of information extraction in retrieval systems, because of the growing demand for search engines that return precise answers to flexible information queries. Advanced retrieval models satisfy that need and they rely on tools that automatically build a probabilistic model of the content of a (multi-media) document. The book focuses on content recognition in text. It elaborates on the past and current most successful algorithms and their application in a variety of domains (e.g., news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text). An important part discusses current statistical and machine learning algorithms for information detection and classification and integrates their results in probabilistic retrieval models. The book also reveals a number of ideas towards an advanced understanding and synthesis of textual content.
1 Information Extraction and Information Technology.- 1.1 Defining Information Extraction.-1.2 Explaining Information Extraction.- 1.3 Information Extraction and Information Retrieval.- 1.4 Information Extraction and Other Information Processing Tasks.- 1.5 The Aims of the Book.- 1.6 Conclusions. -1.7 Bibliography.- 2 Information Extraction from an Historical Perspective.- 2.1 Introduction.- 2.2 A Historical Overview.- 2.3 The Common Extraction Process.- 2.4 A Cascade of Tasks.- 2.5 Conclusions.- 2.6 Bibliography.- 3 The Symbolic Techniques.- 3.1 Introduction.- 3.2 Conceptual Dependency Theory and Scripts.-3.3 Frame Theory.-3.4 Actual Implementations of Symbolic Techniques.- 3.5 Conclusions.- 3.6 Bibliography.- 4 Pattern Recognition.- 4.1 Introduction.- 4.2 What is Pattern Recognition?.- 4.3 The Classification Scheme.- 4.4 The Information Units to Extract.- 4.5 The Features.- 4.6 Conclusions.- 4.7 Bibliography.- 5 Supervised Classification.- 5.1 Introduction.- 5.2 Support Vector Machines.- 5.3 Maximum Entropy Models.- 5.4 Hidden Markov Models.- 5.5 Conditional Random Fields.- 5.6 Decision Rules and Trees.- 5.7 Relational Learning.- 5.8 Conclusions.- 5.9 Bibliography.- 6 Unsupervised Classification Aids.- 6.1 Introduction.- 6.2 Clustering.- 6.3 Expansion.- 6.4 Self-training.- 6.5 Co-training.- 6.6 Active Learning.- 6.7 Conclusions.-6.8 Bibliography.- 7 Integration of Information Extraction in Retrieval Models.- 7.1 Introduction.- 7.2 State of the Art of Information Retrieval.- 7.3 Requirements of Retrieval Systems.- 7.4 Motivation of Incorporating Information Extraction.- 7.5 Retrieval Models.- 7.6 Data Structures.- 7.7 Conclusions.- 7.8 Bibliography.- 8 Evaluation of Information Extraction Technologies.- 8.1 Introduction.- 8.2 Intrinsic Evaluation ofInformation Extraction.- 8.3 Extrinsic Evaluation of Information Extraction in Retrieval.- 8.4 Other Evaluation Criteria.- 8.5 Conclusions.-
8.6 Bibliography.- 9 Case Studies.- 9.1 Introduction.- 9.2 Generic versus Domain Specific Character.- 9.3 Information Extraction from News Texts.- 9.4 Information Extraction from Biomedical Texts.- 9.5 Intelligence Gathering.- 9.6 Information Extraction from Business Texts.- 9.7 Information Extraction from Legal Texts.- 9.8 Information Extraction from Informal Texts.- 9.9 Conclusions.- 9.10 Bibliography.- 10 The Future of Information Extraction in a Retrieval Context.- 10.1 Introduction.- 10.2 The Human Needs and the Machine Performances.- 10.3 Most Important Findings.- 10.4 Algorithmic Challenges.- 10.5 The Future of IE in a Retrieval Context.- 10.6 Bibliography.-
| Erscheint lt. Verlag | 10.10.2006 |
|---|---|
| Reihe/Serie | The Information Retrieval Series | The Information Retrieval Series |
| Zusatzinfo | XIV, 246 p. |
| Verlagsort | Dordrecht |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Sozialwissenschaften ► Kommunikation / Medien ► Buchhandel / Bibliothekswesen | |
| Sozialwissenschaften ► Politik / Verwaltung | |
| Wirtschaft | |
| Schlagworte | algorithms • berck • classification • Cognition • Dom • filtering • Hidden Markov Model • Information Processing • Information Technology • Intelligence • learning • machine learning • Performance |
| ISBN-10 | 1-4020-4993-5 / 1402049935 |
| ISBN-13 | 978-1-4020-4993-4 / 9781402049934 |
| Haben Sie eine Frage zum Produkt? |
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