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1
Greenwashing ou écoblanchiment ? ... : Analyse lexicographique et terminologique d’une tendance de marché ...
Boccuzzi, Celeste. - : Classiques Garnier, 2022
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2
IMIA LaMB WG publications: 'Biomedical Semantics in the Big Data Era' - conference paper introducing the related workshop, MEDINFO, 2015 ...
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IMIA LaMB WG publications: 'Biomedical Semantics in the Big Data Era' - conference paper introducing the related workshop, MEDINFO, 2015 ...
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4
A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018
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5
Automation on Parnassus: CLIO - A Databank Oriented System for Historians [1980]
In: Historical Social Research, Supplement ; 29 ; 113-137 (2018)
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6
A deep learning approach to bilingual lexicon induction in the biomedical domain.
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018
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7
Fichas de los materiales etnográficos fueguinos en museos europeos ; Data sheets of Fuegian ethnographic materials in European museums
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8
Comparing and Combining Portuguese Lexical-Semantic Knowledge Bases
: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2017. : OASIcs - OpenAccess Series in Informatics. 6th Symposium on Languages, Applications and Technologies (SLATE 2017), 2017
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9
Building Semantic Trees from XML Documents
In: ISSN: 1570-8268 ; Journal of Web Semantics ; https://hal-univ-pau.archives-ouvertes.fr/hal-02079156 ; Journal of Web Semantics, Elsevier, 2016, 37-38, pp.1-24. ⟨10.1016/J.WEBSEM.2016.03.002⟩ (2016)
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10
Detailed Phonetic Labeling of Multi-language Database for Spoken Language Processing Applications
In: DTIC (2015)
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11
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing (LG-LP)
Baptista, Jorge; Bhattacharyya, Pushpak; Fellbaum, Christiane. - : HAL CCSD, 2014. : Association for Computational Linguistics, 2014
In: https://hal.archives-ouvertes.fr/hal-01999889 ; Takuya Nakamura. Aug 2014, Dublin, Ireland. Association for Computational Linguistics, 2014, 978-1-873769-44-7. ⟨10.3115/v1/W14-58⟩ ; https://www.aclweb.org/ (2014)
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12
An Annotation Framework for Dense Event Ordering
In: DTIC (2014)
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13
ТЕРМИНОЛОГИЯ, ИСПОЛЬЗУЕМАЯ В НОРМАТИВНЫХ ДОКУМЕНТАХ ПО ОБЩИМ ВОПРОСАМ АНАЛИТИЧЕСКОЙ ХИМИИ И МЕТРОЛОГИИ: РАЗРАБОТКА ИНТЕГРИРОВАННОГО ЭЛЕКТРОННОГО ТЕЗАУРУСА
ШИРОКОВА В.И.; КОЛОТОВ В.П.; АЛЕНИНА М.В.. - : Федеральное государственное унитарное предприятие «Уральский научно-исследовательский институт метрологии», 2013
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14
Improving DoD Energy Efficiency: Combining MMOWGLI Social-Media Brainstorming with Lexical Link Analysis (LLA) to Strengthen the Defense Acquisition Process
In: DTIC (2013)
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15
Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
In: DTIC (2013)
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16
Antipsychotic switching in bipolar disorders: a systematic review.
Grande, Iria; Bernardo Arroyo, Miquel; Bobes García, Julio. - : Cambridge University Press, 2013
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17
Therapeutic hypnosis, psychotherapy, and the digital humanities: the narratives and culturomics of hypnosis, 1800-2008
Rossi, E.; Mortimer, J.; Rossi, K.. - : Amer Soc Clinical Hypnosis, 2013
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18
Synthesis of Systemic Functional Theory & Dynamical Systems Theory for Socio-Cultural Modeling
In: DTIC (2012)
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19
Open-Source Multi-Language Audio Database for Spoken Language Processing Applications
In: DTIC (2012)
Abstract: This report gives a detailed summary of research work completed under Air Force Research Laboratory (AFRL) grant 53925, over the time period (April 12, 2010 April 10, 2012). There are two main aspects of the work completed. First was the collection and annotation of a large open source data base of speech passages from web sites such as You Tube. 300 passages were collected in each of three languages English, Mandarin, and Russian. Approximately 30 hours of speech were collected for each language. Each passage has been carefully transcribed at the phrasal level by human listeners. Each passage was originally transcribed and then checked and the transcription edited as needed by at least two additional native language listeners. The English and Mandarin were then forced aligned and labeled at the phonetic level using a combination of manual and automatic methods. The Russian passages have not yet been marked at the phonetic level. Another phase of the work was to explore several algorithmic methods for improving automatic speech recognition (ASR) for this intelligible but challenging data base. Note that the body of the report has four main sections plus appendices which introduce, describe, and summarize a portion of the work.
Keyword: *PHONETICS; *SPEECH RECOGNITION; AIR FORCE FACILITIES; AIR FORCE RESEARCH; ALGORITHMS; AUTOMATIC; COLLECTION; DATA BASES; ENGLISH LANGUAGE; INTERNET; LABORATORIES; LANGUAGE; Linguistics; PE65502F; PROCESSING; REPORTS; SOUND; SPEECH; Voice Communications; WUAFRL3480BA01
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA571008
http://www.dtic.mil/docs/citations/ADA571008
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20
Advanced Subspace Techniques for Modeling Channel and Session Variability in a Speaker Recognition System
In: DTIC (2012)
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