- Full Description
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.
- Table of Contents
Table of Contents
- 1 Information Extraction and Information Technology.
- 2 Information Extraction from an Historical Perspective.
- 3 The Symbolic Techniques.
- 4 Pattern Recognition.
- 5 Supervised Classification.
- 6 Unsupervised Classification Aids.
- 7 Integration of Information Extraction in Retrieval Models.
- 8 Evaluation of Information Extraction Technologies.
- 9 Case Studies.
- 10 The Future of Information Extraction in a Retrieval Context.
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