@inproceedings{singh-etal-2024-pytlex,
title = "py{TLEX}: A Python Library for {T}ime{L}ine {EX}traction",
author = "Singh, Akul and
Hummer, Jared and
Ocal, Mustafa and
Finlayson, Mark",
editor = "Aletras, Nikolaos and
De Clercq, Orphee",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-demo.4",
pages = "27--34",
abstract = "pyTLEX is an implementation of the TimeLine EXtraction algorithm (TLEX; Finlayson et al.,2021) that enables users to work with TimeML annotations and perform advanced temporal analysis, offering a comprehensive suite of features. TimeML is a standardized markup language for temporal information in text. pyTLEX allows users to parse TimeML annotations, construct TimeML graphs, and execute the TLEX algorithm to effect complete timeline extraction. In contrast to previous implementations (i.e., jTLEX for Java), pyTLEX sets itself apart with a range of advanced features. It introduces a React-based visualization system, enhancing the exploration of temporal data and the comprehension of temporal connections within textual information. Furthermore, pyTLEX incorporates an algorithm for increasing connectivity in temporal graphs, which identifies graph disconnectivity and recommends links based on temporal reasoning, thus enhancing the coherence of the graph representation. Additionally, pyTLEX includes a built-in validation algorithm, ensuring compliance with TimeML annotation guidelines, which is essential for maintaining data quality and reliability. pyTLEX equips researchers and developers with an extensive toolkit for temporal analysis, and its testing across various datasets validates its accuracy and reliability.",
}
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<abstract>pyTLEX is an implementation of the TimeLine EXtraction algorithm (TLEX; Finlayson et al.,2021) that enables users to work with TimeML annotations and perform advanced temporal analysis, offering a comprehensive suite of features. TimeML is a standardized markup language for temporal information in text. pyTLEX allows users to parse TimeML annotations, construct TimeML graphs, and execute the TLEX algorithm to effect complete timeline extraction. In contrast to previous implementations (i.e., jTLEX for Java), pyTLEX sets itself apart with a range of advanced features. It introduces a React-based visualization system, enhancing the exploration of temporal data and the comprehension of temporal connections within textual information. Furthermore, pyTLEX incorporates an algorithm for increasing connectivity in temporal graphs, which identifies graph disconnectivity and recommends links based on temporal reasoning, thus enhancing the coherence of the graph representation. Additionally, pyTLEX includes a built-in validation algorithm, ensuring compliance with TimeML annotation guidelines, which is essential for maintaining data quality and reliability. pyTLEX equips researchers and developers with an extensive toolkit for temporal analysis, and its testing across various datasets validates its accuracy and reliability.</abstract>
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%0 Conference Proceedings
%T pyTLEX: A Python Library for TimeLine EXtraction
%A Singh, Akul
%A Hummer, Jared
%A Ocal, Mustafa
%A Finlayson, Mark
%Y Aletras, Nikolaos
%Y De Clercq, Orphee
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F singh-etal-2024-pytlex
%X pyTLEX is an implementation of the TimeLine EXtraction algorithm (TLEX; Finlayson et al.,2021) that enables users to work with TimeML annotations and perform advanced temporal analysis, offering a comprehensive suite of features. TimeML is a standardized markup language for temporal information in text. pyTLEX allows users to parse TimeML annotations, construct TimeML graphs, and execute the TLEX algorithm to effect complete timeline extraction. In contrast to previous implementations (i.e., jTLEX for Java), pyTLEX sets itself apart with a range of advanced features. It introduces a React-based visualization system, enhancing the exploration of temporal data and the comprehension of temporal connections within textual information. Furthermore, pyTLEX incorporates an algorithm for increasing connectivity in temporal graphs, which identifies graph disconnectivity and recommends links based on temporal reasoning, thus enhancing the coherence of the graph representation. Additionally, pyTLEX includes a built-in validation algorithm, ensuring compliance with TimeML annotation guidelines, which is essential for maintaining data quality and reliability. pyTLEX equips researchers and developers with an extensive toolkit for temporal analysis, and its testing across various datasets validates its accuracy and reliability.
%U https://aclanthology.org/2024.eacl-demo.4
%P 27-34
Markdown (Informal)
[pyTLEX: A Python Library for TimeLine EXtraction](https://aclanthology.org/2024.eacl-demo.4) (Singh et al., EACL 2024)
ACL
- Akul Singh, Jared Hummer, Mustafa Ocal, and Mark Finlayson. 2024. pyTLEX: A Python Library for TimeLine EXtraction. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 27–34, St. Julians, Malta. Association for Computational Linguistics.