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We present a novel framework for automated extraction and approximation of numerical object attributes such as height and weight from the Web. Given an object-attribute pair, we discover and analyze attribute information for a set of comparable objects in order to infer the desired value. This allows us to approximate the desired numerical values even when no exact values can be found in the text. | Extraction and Approximation of Numerical Attributes from the Web Dmitry Davidov ICNC The Hebrew University Jerusalem Israel dmitry@alice.nc.huji.ac.il Ari Rappoport Institute of Computer Science The Hebrew University Jerusalem Israel arir@cs.huji.ac.il Abstract We present a novel framework for automated extraction and approximation of numerical object attributes such as height and weight from the Web. Given an object-attribute pair we discover and analyze attribute information for a set of comparable objects in order to infer the desired value. This allows us to approximate the desired numerical values even when no exact values can be found in the text. Our framework makes use of relation defining patterns and WordNet similarity information. First we obtain from the Web and WordNet a list of terms similar to the given object. Then we retrieve attribute values for each term in this list and information that allows us to compare different objects in the list and to infer the attribute value range. Finally we combine the retrieved data for all terms from the list to select or approximate the requested value. We evaluate our method using automated question answering WordNet enrichment and comparison with answers given in Wikipedia and by leading search engines. In all of these our framework provides a significant improvement. 1 Introduction Information on various numerical properties of physical objects such as length width and weight is fundamental in question answering frameworks and for answering search engine queries. While in some cases manual annotation of objects with numerical properties is possible it is a hard and labor intensive task and is impractical for dealing with the vast amount of objects of interest. Hence there is a need for automated semantic acquisition algorithms targeting such properties. In addition to answering direct questions the ability to make a crude comparison or estimation of object attributes is important as well. For example it .