Knowledge-based extraction of named entities

ABSTRACT

The usual approach to named-entity detection is to learn extraction rules that rely on linguistic, syntactic, or document format patterns that are consistent across a set of documents. However, when there is no consistency among documents, it may be more effective to learn document-specific extraction rules.This paper presents a knowledge-based approach to learning rules for named-entity extraction. Document-specific extraction rules are created using a generate-and-test paradigm and a database of known named-entities. Experimental results show that this approach is effective on Web documents that are difficult for the usual methods.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Algorithms

Keywords:
named-entity extraction