Evaluating Knowledgebased Approaches to the Multilingual Extension of a Temporal Expression Normalizer

The extension to new languages is a well known bottleneck for rule-based systems. Considerable human effort, which typi-cally consists in re-writing from scratch huge amounts of rules, is in fact required to transfer the knowledge available to the system from one language to a new one. Provided sufficient annotated data, ma-chine learning algorithms allow to mini-mize the costs of such knowledge trans-fer but, up to date, proved to be ineffec-tive for some specific tasks. Among these, the recognition and normalization of tem-poral expressions still remains out of their reach. Focusing on this task, and still ad-hering to the rule-based framework, this paper presents a bunch of experiments on the automatic porting to Italian of a system originally developed for Spanish. Differ-ent automatic rule translation strategies are evaluated and discussed, providing a com-prehensive overview of the challenge.