Acquisition of instance attributes via labeled and related instances

Authors:
Enrique Alfonseca;Marius Pasca;Enrique Robledo-Arnuncio
Affiliations:
Google, Zurich, Switzerland;Google, Mountain View, CA, USA;Google, Zurich, Switzerland
Venue:
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Year:
2010

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Abstract

This paper presents a method for increasing the quality of automatically extracted instance attributes by exploiting weakly-supervised and unsupervised instance relatedness data. This data consists of (a) class labels for instances and (b) distributional similarity scores. The method organizes the text-derived data into a graph, and automatically propagates attributes among related instances, through random walks over the graph. Experiments on various graph topologies illustrate the advantage of the method over both the original attribute lists and a per-class attribute extractor, both in terms of the number of attributes extracted per instance and the accuracy of the top-ranked attributes.