General Terms Algorithms, Experimentation
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@MISC{Inc_generalterms,
author = {Google Inc},
title = {General Terms Algorithms, Experimentation},
year = {}
}
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Abstract
Challenging the implicit reliance on document collections, this paper discusses the pros and cons of using query logs rather than document collections, as self-contained sources of data in textual information extraction. The differences are quantified as part of a large-scale study on extracting prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45 % more accurate on average, when acquired from query logs rather than Web documents. Categories and Subject Descriptors
Keyphrases
general term algorithm document collection query log quantifiable property class attribute unstructured text implicit reliance textual information extraction large-scale study lightweight extraction method fuel consumption self-contained source prominent attribute subject descriptor web document top speed head-to-head qualitative comparison
