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).
Takamura and Tsujii employed a combined regression and ranking model with two types of fragmentary clues, including absolute clues and relative clues, to extract numerical attributes of physical objects REF .
Estimating Numerical Attributes by Bringing Together Fragmentary Clues
HLT-NAACL
Computer Science