LORELEI Ilocano Incident Language Pack
Notice bibliographique
Résumé
Introduction LORELEI Ilocano Incident Language Pack (LDC2025T16) was developed by the Linguistic Data Consortium (LDC) and consists of approximately 8.9 million words of Ilocano monolingual text, 3.3 million words of English monolingual text, 3.2 million words of parallel Ilocano-English text, and 3 million words of data annotated for Entity Discovery and Linking and Situation Frames. It contains all of the text data, annotations, supplemental resources, and related software tools for the Ilocano language that were used in the DARPA LORELEI / LoReHLT 2019 Evaluation. 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 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. The evaluation protocol was based on a scenario in which some unforeseen event triggered a need for humanitarian and logistical support in a region where the predominant language was one that had received little or no attention in natural language processing (NLP) research. Evaluation participants provided NLP solutions, including information extraction and machine translation, based on limited resources and with very little time for development. Data Ilocano is spoken in the Philippines. Data was collected in the following genres: news, social network, weblog, newsgroup, discussion forum, and reference material. Entity discovery and linking annotation identified entities to be detected by systems for scoring purposes. Situation frame analysis was designed to extract basic information about needs and relevant issues for planning a disaster response effort. Also included in this release are lexical and grammatical resources as well as three tools: two to recreate original source data from the processed XML material and the other to condition text data users download from X/Twitter. Monolingual, parallel and comparable text are presented in XML with associated dtds. Entity discovery and linking annotation and situation frame annotation are 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). Acknowledgement 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. Updates No updates at this time. Portions © 2019 agenparl.eu, © 2018 American Radio Relay League, © 2016-2019 Amianan Balita Ngayon, © 2019 BBC, © 2018-2019 Bombo Radyo Philippines, © 2018-2019 Cable News Network. Turner Broadcasting System, Inc., © 2018 CBC/Radio-Canada, © 2019 Condé Nast, © 2018 Cordillera Peoples Alliance, © 2018 Crisis Group, © 2019 Dow Jones & Company, Inc., © 2018 Edipresse Media Asia Limited, ©2018 Express Newspapers, © 2018 Galadari Printing and Publishing LLC, © 2018 GardaWorld, © 2019 Got Questions Ministries, © 2018 Guardian News and Media Limited or its affiliated companies, © 2017-2019 Ilocos Sentinel - The Forerunner in Weekly News, © 2019 ISA, International Sociological Association, © 2018 Jpost Inc., © 2018 Los Angeles Times, © 2018 mb.com.ph, © 2018 Microsoft, © 2018 Mindanews, © 2019 National Geographic Society|National Geographic Partners, LLC, © 2018 News Pty Limited, © 2005-2019 Northern Dispatch, © 2018 npr, © 2018 Pacific Media Centre, © 2019 POLITICO LLC, © 2018 primer.com.ph, © 2018-2019 Remate News Central, © 2017-2019 RMN Networks, © 2018 Rogers Media, © 2018 South China Morning Post Publishers Ltd., © 2018 Special Broadcasting Service Corporation, © 2018-2019 SunStar Publishing Inc., © 2016-2019 Tawid News Magazine, © 2019 Telegraph Media Group Limited, © 2018 The Irish Times, © 2019 The New York Times Company, © 2018 The Social Justice Foundation, © 2018 The Times of Israel, © 2018 Toronto Star Newspapers Ltd., © 2019 United Methodist Communications, © 2018 United Nations Office for the Coordination of Humanitarian Affairs, © 2018 United Press International, Inc., © 2019 Vox Media, Inc., © 2018 Wells Media Group, Inc., © 2018 Winslow Record, © 2019, 2025 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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,121 | 0,086 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».