Evaluation of the Automatic Multilinguality for Time Expression Resolution
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Automatic resolution rule assignment to multilingual Temporal Expressions using annotated corpora
TIME '06 Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning
Event ordering using TERSEO system
Data & Knowledge Engineering - Special issue: Application of natural language to information systems (NLDB04)
Multilingual extension of a temporal expression normalizer using annotated corpora
CrossLangInduction '06 Proceedings of the International Workshop on Cross-Language Knowledge Induction
Automatic time expression labeling for english and chinese text
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Information Sciences: an International Journal
ID 392: TERSEO + T2T3 Transducer: a systems for recognizing and normalizing TIMEX3
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Time for More Languages: Temporal Tagging of Arabic, Italian, Spanish, and Vietnamese
ACM Transactions on Asian Language Information Processing (TALIP)
Hi-index | 0.00 |
The extension to new languages is a well known bottleneck for rule-based systems. Considerable human effort, which typically 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, machine learning algorithms allow to minimize the costs of such knowledge transfer but, up to date, proved to be ineffective for some specific tasks. Among these, the recognition and normalization of temporal expressions still remains out of their reach. Focusing on this task, and still adhering 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. Different automatic rule translation strategies are evaluated and discussed, providing a comprehensive overview of the challenge.