LORELEI Ukrainian Representative Language Pack
Notice bibliographique
Résumé
<br><br> Introduction <br><br> <br><br> LORELEI Ukrainian Representative Language Pack Ukrainian-English parallel and comparative text, annotations, supplemental resources and related software tools developed by LDC for the DARPA LORELEI program. <br><br> <br><br> The LORELEI (Low Resource Languages for Emergent Incidents) program was concerned with building human language technology for low resource languages in the context of emergent situations like natural disasters or disease outbreaks. Linguistic resources for LORELEI include Representative Language Packs and Incident Language Packs for over two dozen low resource languages, comprising data, annotations, basic natural language processing tools, lexicons and grammatical resources. Representative languages were selected to provide broad typological coverage, while incident languages were selected to evaluate system performance on a language whose identity was disclosed at the start of the evaluation. <br><br> Data <br><br> <br><br> Ukrainian is spoken mainly in Ukraine. Data was collected in the following genres: discussion forum, news, reference, social network, and weblog. Both monolingual text collection and parallel text creation involved a combination of manual and automatic methods. <br><br> <br><br> Data volumes are as follows: <br><br> <br><br> 111 million words of Ukrainian monolingual text, approximately 700,000 words of which were translated into English <br><br> 86,000 Ukrainian words translated from English data <br><br> 174,000 words of found parallel text <br><br> Over 2,000,000 words of comparable text <br><br> <br><br> Approximately 75,000 words were annotated for named entities, and up to 50,000 words contain additional annotation, including situation frames (identifying entities, needs and issues) and entity detection and linking. <br><br> <br><br> Lexical resources and software tools are also included in this release. The tools recreate source data from the processed XML material, condition text data users download from Twitter, apply sentence segmentation to raw text and tag named entities. <br><br> <br><br> Monolingual and parallel text are presented in XML with associated dtds. Annotation data is presented as tab delimited files. All text is UTF-8 encoded. The knowledge base for entity linking annotation for this corpus and all LORELEI Representative Language and Incident Language Packs is available separately as LORELEI Entity Detection and Linking Knowledge Base (LDC2020T10). <br><br> Acknowledgement <br><br> <br><br> This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-15-C-0123. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA. <br><br> Samples <br><br> <br><br> Please view the following samples: <br><br> <br><br> Ukrainian LTF XML <br><br> Ukrainian PSM XML <br><br> English A LTF XML <br><br> English A PSM XML <br><br> English B LTF XML <br><br> English B PSM XML <br><br> Simple Name Entity Annotation (XML) <br><br> Full Named Entity Annotation (XML) <br><br> Needs Annotation (TXT) <br><br> Entity Annotation (TXT) <br><br> <br><br> Updates <br><br> <br><br> None at this time. <br><br> Copyright <br><br> Portions © 2002-2007, 2009-2010 Agence France Presse, © 2014-2017 Agroindustrial Association of Ukraine, © 2000 American Broadcasting Company, © 2014, 2016 BBC, © 2000 Cable News Network. LP, LLLP, © 2014-2017 CASE Ukraine, © 2008 Central News Agency (Taiwan), © 2016 Channel 5, © 2010-2016 CSLR, © 2012 Daily Lviv, © 2015 Depo.ua, © 1989 Dow Jones & Company, Inc., © 2012 Ecology of Life, © 2016 Espreso.tv, © 2015-2016 euronews, © 2014-2016 European Truth-eurointegration.com.ua, © 2015-2016 expres.online, © 2015-2016 FACTS.ICTV, © 2013, 2016 Gazeta.ua, © 2016 High Castle Publishing House LLC, © 2016 Hromadske Radio, © 2016 IHA Ukrainian News, © 2014 Information Agency LIGABiznesInform, © 2009-2017 Institute of World Policy, © 2012-2013 iPress.ua, © 2016 JSC Lux Television and Radio Company-Zaxid.net, © 2010-2011, 2016 Keprate Partners, © 2015-2016, Korrespondent.net, © 2016 LB.ua, © 2015-2016 LLC UBT, © 2005 Los Angeles Times-Washington Post News Service, Inc., © 2016 MEDIA-DK PUBLISHING HOUSE LLC, © 2010 Mediastar, © 2016 Mirror of the week-Ukraine, © 2016 MyInforms.com, © 2000 National Broadcasting Company, Inc., © 2015 National Information Systems LLC, © 2016 NavkoloNas.com, © 1999, 2005, 2006, 2010 New York Times, © 2016 PJSC Lux Television and Radio Company-Radio Maximum, © 2015 Polskie Radio S.A., © 2000 Public Radio International, © 2016 RFE/RL, © 2016 segodnya.ua, © 2010-2017 SFTC Ukrinterenergo, © 2003, 2005-2008, 2010 The Associated Press, © 2016-2017 The National Radio Company of Ukraine, © 2015-2016 TSN.ua, © 2016 uapress, © 2010-2015 Ukraine Municipal Local Economic Development Project - Federation of Canadian Municipalities, © 2016 Ukrainian National News-news agency, © 2010, 2014-2016 Ukrainian Truth, © 2016 Ukrainians in Portugal-Union of Ukrainians in Portugal, © 2015-2016 Ukrinform, © 2010-2014 UNIAN.NET, © 2016 Week.ua, © 2016 Western Information Corporation, © 2003, 2005-2008 Xinhua News Agency, © 2016 ZNAJ.UA, © 2020 Trustees of the University of Pennsylvania
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,087 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».