Multilingual Recognition of Temporal Expressions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00117840" target="_blank" >RIV/00216224:14330/20:00117840 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Multilingual Recognition of Temporal Expressions
Original language description
The paper presents a multilingual approach to temporal expression recognition (TER) using existing tools and their combination. We observe that the rules based methods perform well on documents using wellformed temporal expressions in a narrower domain (e.g., news), while data driven methods are more stable within less standard language and texts across domains. With combination of the two approaches, we achieved F1 of 0.73 and 0.9 for strict and relaxed evaluations respectively on one English dataset. Although these results do not achieve the state-of-the-art on English, the same method outperformed the state-of-the-art results in a multilingual setting not only in recall but also in F1. We see this as a strong indication that combining rule based systems with data driven models such as BERT is a valid approach to improve the overall performance in TER, especially for languages other than English. Further observations indicate that in the domain of office documents, the combined method is able to recognize general temporal expressions as well as domain specific ones (e.g., those used in financial documents).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LM2018101" target="_blank" >LM2018101: Digital Research Infrastructure for the Language Technologies, Arts and Humanities</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Fourteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2020
ISBN
9788026316008
ISSN
2336-4289
e-ISSN
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Number of pages
12
Pages from-to
67-78
Publisher name
Tribun EU
Place of publication
Brno
Event location
Brno
Event date
Jan 1, 2020
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
000655471300007