Using structured text for large-scale attribute extraction

ABSTRACT

We propose a weakly-supervised approach for extracting class attributes from structured text available within Web documents. The overall precision of the extracted attributes is around 30% higher than with previous methods operating on Web documents. In addition to attribute extraction, this approach also automatically identifies values for a subset of the extracted class attributes.

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INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing

General Terms:
Algorithms, Experimentation

Keywords:
class attribute extraction, knowledge acquisition, structured text collections, weakly-supervised information extraction