Argitalpenak

In Proceedings of the 28th Conference on Computational Natural Language Learning, pages 105–116, Miami, FL, USA. Association for Computational Linguistics.

Nayla Escribano, German Rigau, Rodrigo Agerri,
A modular approach for multilingual timex detection and normalization using deep learning and grammar-based methods,
Knowledge-Based Systems,
Volume 273,
2023,
110612,
ISSN 0950-7051,
https://doi.org/10.1016/j.knosys.2023.110612.
(https://www.sciencedirect.com/science/article/pii/S0950705123003623)
Abstract: Detecting and normalizing temporal expressions is an essential step for many NLP tasks. While a variety of methods have been proposed for detection, best normalization approaches rely on hand-crafted rules. Furthermore, most of them have been designed only for English. In this paper we present a modular multilingual temporal processing system combining a fine-tuned Masked Language Model for detection, and a grammar-based normalizer. We experiment in Spanish and English and compare with HeidelTime, the state-of-the-art in multilingual temporal processing. We obtain best results in gold timex normalization, timex detection and type recognition, and competitive performance in the combined TempEval-3 relaxed value metric. A detailed error analysis shows that detecting only those timexes for which it is feasible to provide a normalization is highly beneficial in this last metric. This raises the question of which is the best strategy for timex processing, namely, leaving undetected those timexes for which is not easy to provide normalization rules or aiming for high coverage.
Keywords: Temporal processing; Multilingualism; Sequence labeling; Grammar-based approaches; Deep learning; Natural language processing

Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3382–3390, Marseille, France. European Language Resources Association.

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, USA, pp. 748-752

Procesamiento del Lenguaje Natural 53: 155-158. http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/download/50...

LREC 2014: 1155-1161

LREC 2014: 3823-3828. ISBN 978-2-9517408-8-4