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Mining an English-Chinese parallel Dataset of Financial News
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In: Journal of Open Humanities Data; Vol 8 (2022); 9 ; 2059-481X (2022)
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The rumour spectrum
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In: ISSN: 1932-6203 ; EISSN: 1932-6203 ; PLoS ONE ; https://hal.archives-ouvertes.fr/hal-01691934 ; PLoS ONE, Public Library of Science, 2018, 13 (1), pp.e0189080.1-27. ⟨10.1371/journal.pone.0189080⟩ (2018)
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A semi-supervised Learning Approach to find equivalent long-string Organization Names
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In: Colloque- Forum PEPS EXIA ; https://hal-enpc.archives-ouvertes.fr/hal-02310298 ; Colloque- Forum PEPS EXIA, Oct 2016, Champs sur Marne, France. 2016 (2016)
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Clustering and Relational Ambiguity: from Text Data to Natural Data
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In: EISSN: 2416-5999 ; Journal of Data Mining and Digital Humanities ; https://hal.archives-ouvertes.fr/hal-00920423 ; Journal of Data Mining and Digital Humanities, Episciences.org, 2013, 1 (1), pp.1 (2013)
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Modeling Noun-Phrases Dynamics in Specialized Text Collections
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In: ISSN: 0929-6174 ; Journal of Quantitative Linguistics ; https://hal.archives-ouvertes.fr/hal-02054488 ; Journal of Quantitative Linguistics, Taylor & Francis (Routledge), 2010, 17 (3), pp.212-228. ⟨10.1080/09296174.2010.485447⟩ (2010)
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Abstract:
The science of biology has entered a new era with new approaches for information processing frameworks and high-throughput experiments. This has led to a high rate of publication production and the emergence of large accessible databases in English, permitting the creation of text collections in any specialized domain. To process such text data, systematic analysis of language properties is helpful and benefits from a distribution description. In this article, firstly, as scientific publications are time-stamped we can analyse distribution profiles of noun-phrases (i.e. “content-words”) over time. Hence, time-dependency analysis of noun-phrases reveals interesting specific behaviour taking into account sequential occurrence of features. Single content-word distributions appear to be linearly shaped. We also observed that the association of content-words is distributed in a different way over time, i.e. as a mixed beta distribution.
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Keyword:
[SHS.SOCIO]Humanities and Social Sciences/Sociology
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URL: https://doi.org/10.1080/09296174.2010.485447
https://hal.archives-ouvertes.fr/hal-02054488
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Bayesian Discriminant Analysis for Lexical Semantic Tagging
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In: European Meeting on Cybernetics and Systems Research (EMCSR) ; https://hal.archives-ouvertes.fr/hal-03373905 ; European Meeting on Cybernetics and Systems Research (EMCSR), Apr 2002, Vienne, Austria (2002)
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