The role of documents vs. queries in extracting class attributes from text

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 H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing; I.2.7 [Artificial Intelligence]: Natural Language Processing; I.2.6 [Artificial Intelligence]: Learning; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Experimentation Keywords Knowledge acquisit...

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Marius Pasca, Benjamin Van Durme, Nikesh Garera
• Keywords: CIKM 2007, Class Attribute, Document Collections, Query Logs