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| E. Saquete, P. Martinez-Barco, R. Munoz, M. Negri, M. Speranza, R. Sprugnoli, "Automatic resolution rule assignment to multilingual Temporal Expressions using annotated corpora," Temporal Representation and Reasoning, International Syposium on, pp. 218-224, Thirteenth International Symposium on Temporal Representation and Reasoning (TIME'06), 2006. | |||
| BibTex | x | ||
| @article{ 10.1109/TIME.2006.9, author = {E. Saquete and P. Martinez-Barco and R. Munoz and M. Negri and M. Speranza and R. Sprugnoli}, title = {Automatic resolution rule assignment to multilingual Temporal Expressions using annotated corpora}, journal ={Temporal Representation and Reasoning, International Syposium on}, volume = {0}, year = {2006}, issn = {1530-1311}, pages = {218-224}, doi = {http://doi.ieeecomputersociety.org/10.1109/TIME.2006.9}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Temporal Representation and Reasoning, International Syposium on TI - Automatic resolution rule assignment to multilingual Temporal Expressions using annotated corpora SN - 1530-1311 SP218 EP224 A1 - E. Saquete, A1 - P. Martinez-Barco, A1 - R. Munoz, A1 - M. Negri, A1 - M. Speranza, A1 - R. Sprugnoli, PY - 2006 KW - null VL - 0 JA - Temporal Representation and Reasoning, International Syposium on ER - | |||
The knowledge-based system TERSEO was originally developedfor the Recognition and Normalization of temporal expressions in Spanish and then extended to other languages: to English first, through the automatic translation of the temporal expressions, and then to Italian, applying a porting process where the automatic translation of the rules was combined with the extraction of expressionsfrom an annotated corpus.
In this paper we present a new automatic porting procedure, where resolution rules are atltomatically assigned to the tenporal expressions that have been acquired in a new language, thus eliminating the need for automatic translation and consequently minimizing the errors produced. This is achieved by exploiting the rules of the temporal model, which are language independent, and the information extracted from the annotated corpus. Evaluation results of the updated version of TERSEO for English show a considerable improvement in recognition perlformance (+ 14% F-Measure) with respect to the original system.