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Information Extraction in the Web Era

Natural Language Communication for Knowledge Acquisition and Intelligent Information Agents

  • Maria Teresa Pazienza

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2700)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2700)

Table of contents

  1. Front Matter
  2. Information Extraction in the Web Era

    1. Roman Yangarber
      Pages 1-28
    2. Béatrice Daille
      Pages 29-44
    3. Toru Hisamitsu, Jun-ichi Tsujii
      Pages 45-76
    4. Maria Teresa Pazienza, Michele Vindigni
      Pages 92-128
  3. Back Matter

About this book

Introduction

The number of research topics covered in recent approaches to Information - traction (IE) is continually growing as new facts are being considered. In fact, while the user’s interest in extracting information from texts deals mainly with the success of the entire process of locating, in document collections, facts of interest, the process itself is dependent on several constraints (e.g. the domain, the collection dimension and location, and the document type) and currently it tackles composite scenarios, including free texts, semi- and structured texts such as Web pages, e-mails, etc. The handling of all these factors is tightly related to the continued evolution of the underlying technologies. In the last few years, in real-world applications we have seen the need for scalable, adaptable IE systems (see M.T.Pazienza, “InformationExtraction: Towards Scalable Adaptable Systems”, LNAI 1714) to limit the need for human intervention in the customization process and portability of the IE application to new domains. Scalability and adaptability requirements are still valid impacting features and get more relevance into a Web scenario, where in intelligent information agents are expected to automatically gather information from heterogeneous sources.

Keywords

DOM Information Retrieval Web data mining content summarization data analysis data mining digital libraries information extraction knowledge discovery knowledge extraction natural language processing semi-structured data text mining

Editors and affiliations

  • Maria Teresa Pazienza
    • 1
  1. 1.DISP, University of Tor VergataRomeItaly

Bibliographic information

  • DOI https://doi.org/10.1007/b11781
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-40579-5
  • Online ISBN 978-3-540-45092-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site