A modular approach for multilingual timex detection and normalization using deep learning and grammar-based methods-Reference-Cited by-同舟云学术

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A modular approach for multilingual timex detection and normalization using deep learning and grammar-based methods

Published:2023-08 Issue: Volume:273 Page:110612
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Escribano Nayla^ORCID,Rigau German,Agerri Rodrigo^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference33 articles.

1. SemEval-2015 task 5: QA TempEval - evaluating temporal information understanding with question answering;Llorens,2015

2. TREC 2015 temporal summarization track overview;Aslam,2015

3. SemEval-2017 task 12: Clinical TempEval;Bethard,2017

4. SemEval-2013 task 1: TempEval-3: Evaluating time expressions, events, and temporal relations;UzZaman,2013

5. J. Strötgen, J. Zell, M. Gertz, HeidelTime: Tuning English and developing Spanish resources for TempEval-3, in: Proc. of the 7th International Workshop on Semantic Evaluation, 2013, pp. 15–19.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. XTime: A general rule-based method for time expression recognition and normalization;Knowledge-Based Systems;2024-08

2. Time expression as update operations: Normalizing time expressions via a distantly supervised neural semantic parser;Knowledge-Based Systems;2023-10

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