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

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.

Acquiring knowledge about human goals from Search Query Logs

“…While our research is related to query log analysis (cf. for example (Broder, 2002;Jansen et al, 2008;Lee et al, 2005;Murray & Teevan, 2007;Pasca et al, 2007)), our goal is to contribute to the problem of commonsense knowledge acquisition.…”

Using structured text for large-scale attribute extraction

“…• Frequency-based ranking: One method is to rank the selected candidates for a particular class, by frequency [10] as shown in Equation 1. Each candidate P extracted for a class C is assigned a score based on the number of instances I from C that produced P .…”

“…The strategy for assigning correctness labels is similar to the assessment method used in previous work [10]. An attribute is "vital" if it must be present in an ideal list of attributes for the target class; "okay" if it provides useful but non-essential information; and "wrong" if it is incorrect.…”

“…(Dpatt) -Previous approach with unstructured text: We also implemented a third system using the attribute extraction strategy described in previous work [10]. This approach uses hand-crafted patterns (such as X-of-Y patterns) to extract attributes from unstructured text within Web documents.…”

“…Most of these approaches are constrained to specific domains or particular target relations [3] specified in advance, and therefore do not gracefully scale to more generic classes and applications for other domains. A previous approach for attribute extraction from Web document collections looks at the immediate textual vicinity of instances (e.g., Austria) from a target class (e.g., Country) to identify potential attributes of the class [10]. However, the attributes that can be extracted using this technique are limited to specific types, following certain patterns (e.g., X-of-Y patterns [15]).…”