The fingerprint of human referring expressions and their surface realization with graph transducers
Bernd Bohnet · 2008
The algorithm IS-FP takes up the idea from the IS-FBN algorithm developed for the shared task 2007. Both algorithms learn the individual attribute selection style for each human that provided referring expressions to the corpus. The IS-FP algorithm was developed with two additional goals (1) to improve the indentification time that was poor for the FBN algorithm and (2) to push the dice score even higher. In order to generate a word string for the selected attributes, we build based on individual preferences a surface syntactic dependency tree as input. We derive the individual preferences from the training set. Finally, a graph transducer maps the input strucutre to a deep morphologic structure.