Estimating Numerical Attributes by Bringing Together Fragmentary Clues

Hiroya Takamura, Jun'ichi Tsujii

Abstract: This work is an attempt to automatically obtain numerical attributes of physical objects. We propose representing each physical object as a feature vector and representing sizes as linear functions of feature vectors. We train the function in the framework of the combined regression and ranking with many types of fragmentary clues including absolute clues (e.g., A is 30cm long) and relative clues (e.g., A is larger than B).